From d08828e6ade74ccf70f11ad36033a9b78ab37bed Mon Sep 17 00:00:00 2001
From: Pratzybha <84196704+Pratzybha@users.noreply.github.com>
Date: Sat, 26 Oct 2024 21:16:29 +0530
Subject: [PATCH 1/4] Add files via upload

---
 .../Dataset/Onlinedeliverydata.csv            |  391 ++++
 .../Dataset/README.md                         |   13 +
 .../Images/AgeGroup.png                       |  Bin 0 -> 18995 bytes
 .../Images/CorrelationCalculation.png         |  Bin 0 -> 110235 bytes
 .../Images/DemographicDetails.png             |  Bin 0 -> 67365 bytes
 .../Images/FeaturesOnlineDelivery 1.png       |  Bin 0 -> 93681 bytes
 .../Images/FeaturesOnlineDelivery 2.png       |  Bin 0 -> 84148 bytes
 .../Images/FeaturesOnlineDelivery 3.png       |  Bin 0 -> 104255 bytes
 .../Model/Model1.ipynb                        | 1614 ++++++++++++++++
 .../Model/Model2.ipynb                        | 1638 +++++++++++++++++
 .../Model/README.md                           |  157 ++
 .../requirements.txt.txt                      |   10 +
 12 files changed, 3823 insertions(+)
 create mode 100644 Online Food Delivery Preferences/Dataset/Onlinedeliverydata.csv
 create mode 100644 Online Food Delivery Preferences/Dataset/README.md
 create mode 100644 Online Food Delivery Preferences/Images/AgeGroup.png
 create mode 100644 Online Food Delivery Preferences/Images/CorrelationCalculation.png
 create mode 100644 Online Food Delivery Preferences/Images/DemographicDetails.png
 create mode 100644 Online Food Delivery Preferences/Images/FeaturesOnlineDelivery 1.png
 create mode 100644 Online Food Delivery Preferences/Images/FeaturesOnlineDelivery 2.png
 create mode 100644 Online Food Delivery Preferences/Images/FeaturesOnlineDelivery 3.png
 create mode 100644 Online Food Delivery Preferences/Model/Model1.ipynb
 create mode 100644 Online Food Delivery Preferences/Model/Model2.ipynb
 create mode 100644 Online Food Delivery Preferences/Model/README.md
 create mode 100644 Online Food Delivery Preferences/requirements.txt.txt

diff --git a/Online Food Delivery Preferences/Dataset/Onlinedeliverydata.csv b/Online Food Delivery Preferences/Dataset/Onlinedeliverydata.csv
new file mode 100644
index 000000000..72574f175
--- /dev/null
+++ b/Online Food Delivery Preferences/Dataset/Onlinedeliverydata.csv	
@@ -0,0 +1,391 @@
+Age,Gender,Marital Status,Occupation,Monthly Income,Educational Qualifications,Family size,latitude,longitude,Pin code,Medium (P1),Medium (P2),Meal(P1),Meal(P2),Perference(P1),Perference(P2),Ease and convenient,Time saving,More restaurant choices,Easy Payment option,More Offers and Discount,Good Food quality,Good Tracking system,Self Cooking,Health Concern,Late Delivery,Poor Hygiene,Bad past experience,Unavailability,Unaffordable,Long delivery time,Delay of delivery person getting assigned,Delay of delivery person picking up food,Wrong order delivered,Missing item,Order placed by mistake,Influence of time,Order Time,Maximum wait time,Residence in busy location,Google Maps Accuracy,Good Road Condition,Low quantity low time,Delivery person ability,Influence of rating,Less Delivery time,High Quality of package,Number of calls,Politeness,Freshness ,Temperature,Good Taste ,Good Quantity,Output,Reviews
+20,Female,Single,Student,No Income,Post Graduate,4,12.9766,77.5993,560001,Food delivery apps, Web browser,Breakfast, Lunch,Non Veg foods (Lunch / Dinner), Bakery items (snacks),Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Agree,Agree,Agree,Agree,Agree,Agree,Yes,Weekend (Sat & Sun),30 minutes,Agree,Neutral,Neutral,Neutral,Neutral,Yes,Moderately Important,Moderately Important,Moderately Important,Moderately Important,Moderately Important,Moderately Important,Moderately Important,Moderately Important,Yes,"Nil
+"
+24,Female,Single,Student,Below Rs.10000,Graduate,3,12.977,77.5773,560009,Food delivery apps, Web browser,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Neutral,Agree,Strongly agree,Strongly agree,Agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Yes,Anytime (Mon-Sun),30 minutes,Strongly Agree,Neutral,Disagree,Strongly disagree,Agree,Yes,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Yes,Nil
+22,Male,Single,Student,Below Rs.10000,Post Graduate,3,12.9551,77.6593,560017,Food delivery apps, Direct call,Lunch, Snacks,Non Veg foods (Lunch / Dinner), Ice cream / Cool drinks,Strongly agree,Strongly agree,Strongly agree,Neutral,Neutral,Disagree,Neutral,Disagree,Neutral,Neutral,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Strongly agree,Agree,Neutral,Yes,Anytime (Mon-Sun),45 minutes,Agree,Strongly Agree,Neutral,Neutral,Agree,Yes,Important,Very Important,Moderately Important,Very Important,Very Important,Important,Very Important,Moderately Important,Yes,"Many a times payment gateways are an issue, so getting the refund and the surcharges are an inconvenience"
+22,Female,Single,Student,No Income,Graduate,6,12.9473,77.5616,560019,Food delivery apps, Walk-in,Snacks, Dinner,Veg foods (Breakfast / Lunch / Dinner), Bakery items (snacks),Agree,Agree,Strongly agree,Agree,Strongly agree,Agree,Agree,Agree,Strongly agree,Neutral,Agree,Disagree,Disagree,Neutral,Agree,Agree,Agree,Disagree,Disagree,Neutral,Yes,Anytime (Mon-Sun),30 minutes,Disagree,Agree,Agree,Neutral,Agree,Yes,Very Important,Important,Moderately Important,Very Important,Very Important,Very Important,Very Important,Important,Yes,nil
+22,Male,Single,Student,Below Rs.10000,Post Graduate,4,12.985,77.5533,560010,Walk-in, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Agree,Agree,Neutral,Neutral,Agree,Strongly agree,Strongly agree,Agree,Strongly agree,Agree,Disagree,Strongly agree,Strongly agree,Neutral,Neutral,Neutral,Disagree,Yes,Weekend (Sat & Sun),30 minutes,Agree,Agree,Agree,Agree,Agree,Yes,Important,Important,Moderately Important,Important,Important,Important,Very Important,Very Important,Yes,NIL
+27,Female,Married,Employee,More than 50000,Post Graduate,2,12.9299,77.6848,560103,Food delivery apps, Walk-in,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Neutral,Agree,Agree,Neutral,Agree,Disagree,Neutral,Agree,Agree,Agree,Agree,Agree,Strongly agree,Strongly agree,Strongly agree,Agree,Strongly agree,Strongly agree,Yes,Anytime (Mon-Sun),45 minutes,Neutral,Agree,Neutral,Neutral,Neutral,Yes,Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Yes,NIL
+22,Male,Single,Student,No Income,Graduate,3,12.977,77.5773,560009,Food delivery apps, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Strongly agree,Agree,Strongly agree,Agree,Neutral,Agree,Disagree,Agree,Disagree,Agree,Disagree,Strongly agree,Strongly disagree,Neutral,Disagree,Disagree,Strongly agree,Strongly agree,Strongly agree,No,Anytime (Mon-Sun),45 minutes,Agree,Strongly Agree,Disagree,Agree,Strongly Agree,Yes,Very Important,Very Important,Unimportant,Important,Very Important,Very Important,Very Important,Very Important,Yes,Nil
+24,Female,Single,Student,No Income,Post Graduate,3,12.9828,77.6131,560042,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Ice cream / Cool drinks,Strongly agree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Neutral,Neutral,Neutral,Neutral,Agree,Agree,Agree,Neutral,Agree,Agree,Yes,Weekend (Sat & Sun),45 minutes,Agree,Agree,Agree,Neutral,Disagree,Maybe,Moderately Important,Important,Moderately Important,Slightly Important,Important,Moderately Important,Very Important,Very Important,Yes,NIL
+23,Female,Single,Student,No Income,Post Graduate,2,12.9766,77.5993,560001,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Agree,Agree,Neutral,Agree,Agree,Agree,Neutral,Agree,Neutral,Neutral,Disagree,Agree,Agree,Agree,Agree,Agree,Strongly agree,Yes,Anytime (Mon-Sun),30 minutes,Disagree,Disagree,Neutral,Agree,Agree,Maybe,Important,Important,Important,Important,Important,Important,Important,Important,Yes,Nil
+23,Female,Single,Student,No Income,Post Graduate,4,12.9854,77.7081,560048,Food delivery apps, Walk-in,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Agree,Agree,Neutral,Neutral,Neutral,Neutral,Neutral,Agree,Agree,Agree,Neutral,Agree,Agree,Yes,Weekend (Sat & Sun),45 minutes,Agree,Agree,Agree,Disagree,Disagree,Yes,Moderately Important,Very Important,Moderately Important,Moderately Important,Moderately Important,Moderately Important,Very Important,Very Important,Yes,Nil
+22,Female,Single,Student,No Income,Post Graduate,5,12.985,77.5533,560010,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Agree,Agree,Neutral,Agree,Agree,Strongly agree,Agree,Strongly agree,Agree,Agree,Agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Agree,Strongly Agree,Neutral,Agree,Yes,Important,Very Important,Important,Important,Very Important,Very Important,Very Important,Very Important,Yes,Nil
+23,Male,Single,Student,Below Rs.10000,Post Graduate,2,12.977,77.5773,560009,Food delivery apps, Walk-in,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Strongly agree,Strongly agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Strongly agree,Disagree,Agree,Strongly agree,Strongly agree,Neutral,Strongly agree,Strongly agree,Agree,Strongly agree,Strongly agree,Disagree,Maybe,Weekend (Sat & Sun),45 minutes,Strongly Agree,Disagree,Agree,Disagree,Strongly Agree,Yes,Important,Very Important,Important,Very Important,Very Important,Important,Very Important,Very Important,Yes,Language barrier is also one major issue. Moslty these delivery boys are only familiar with Kanada which creates a problem while addressing any issue.
+23,Male,Single,Student,No Income,Post Graduate,5,12.8988,77.5764,560078,Walk-in, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Bakery items (snacks),Agree,Neutral,Agree,Agree,Agree,Disagree,Agree,Neutral,Agree,Neutral,Agree,Disagree,Disagree,Neutral,Agree,Neutral,Neutral,Disagree,Strongly disagree,Strongly disagree,Yes,Weekend (Sat & Sun),30 minutes,Agree,Agree,Neutral,Disagree,Agree,Maybe,Slightly Important,Unimportant,Slightly Important,Moderately Important,Moderately Important,Moderately Important,Moderately Important,Slightly Important,Yes,Nil
+21,Male,Single,Student,No Income,Graduate,4,12.977,77.5773,560009,Walk-in, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Disagree,Strongly agree,Neutral,Neutral,Neutral,Disagree,Disagree,Agree,Agree,Agree,Strongly disagree,Disagree,Strongly disagree,No,Weekend (Sat & Sun),30 minutes,Agree,Neutral,Disagree,Strongly Agree,Agree,Yes,Important,Important,Slightly Important,Moderately Important,Very Important,Very Important,Very Important,Very Important,Yes,Nil
+23,Female,Single,Self Employeed,10001 to 25000,Post Graduate,5,12.9438,77.5738,560004,Food delivery apps, Walk-in,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Sweets,Agree,Agree,Strongly agree,Agree,Neutral,Neutral,Neutral,Agree,Strongly agree,Disagree,Strongly agree,Strongly agree,Strongly agree,Neutral,Agree,Agree,Neutral,Agree,Agree,Disagree,Yes,Anytime (Mon-Sun),45 minutes,Neutral,Strongly Agree,Strongly Agree,Disagree,Agree,Yes,Moderately Important,Important,Moderately Important,Important,Very Important,Important,Very Important,Very Important,Yes,NIL
+24,Female,Single,Student,No Income,Post Graduate,6,12.8893,77.6399,560068,Food delivery apps, Walk-in,Lunch, Snacks,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Agree,Strongly agree,Strongly agree,Neutral,Agree,Strongly agree,Strongly agree,Strongly agree,Disagree,Disagree,Disagree,Neutral,Disagree,Neutral,Agree,Strongly agree,Strongly agree,Strongly agree,Neutral,Maybe,Anytime (Mon-Sun),30 minutes,Strongly Agree,Strongly Agree,Neutral,Disagree,Strongly Agree,Yes,Moderately Important,Moderately Important,Moderately Important,Important,Very Important,Very Important,Very Important,Very Important,Yes,NIL
+28,Female,Single,Employee,25001 to 50000,Post Graduate,2,12.9783,77.6408,560038,Walk-in, Direct call,Dinner, Dinner,Veg foods (Breakfast / Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Neutral,Neutral,Neutral,Agree,Neutral,Neutral,Agree,Disagree,Neutral,Disagree,Neutral,Neutral,Neutral,Disagree,Disagree,Disagree,Disagree,Agree,Agree,Agree,No,Anytime (Mon-Sun),60 minutes,Neutral,Neutral,Agree,Agree,Agree,No,Unimportant,Unimportant,Unimportant,Slightly Important,Very Important,Moderately Important,Important,Important,Yes,NIL
+23,Female,Single,Student,No Income,Graduate,3,12.982,77.6256,560008,Food delivery apps, Walk-in,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Disagree,Disagree,Disagree,Agree,Neutral,Neutral,Neutral,Strongly agree,Neutral,Disagree,Strongly disagree,Disagree,Disagree,Disagree,Agree,Disagree,Agree,Yes,Anytime (Mon-Sun),45 minutes,Disagree,Agree,Neutral,Agree,Neutral,Yes,Important,Very Important,Slightly Important,Important,Very Important,Very Important,Very Important,Important,Yes,"Spillage, bad packaging and missing items "
+25,Male,Single,Student,No Income,Graduate,4,12.8988,77.5764,560078,Food delivery apps, Walk-in,Lunch, Snacks,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Neutral,Agree,Strongly disagree,Strongly agree,Neutral,Neutral,Strongly disagree,Strongly agree,Strongly disagree,Strongly agree,Neutral,Neutral,Neutral,Yes,Anytime (Mon-Sun),45 minutes,Strongly Agree,Strongly Agree,Agree,Strongly disagree,Strongly Agree,Yes,Slightly Important,Very Important,Important,Very Important,Very Important,Very Important,Very Important,Very Important,Yes,Once my order from kfc got exchanged with someone else. Fault was delivery boy's. 
+21,Female,Single,Student,Below Rs.10000,Post Graduate,1,12.9783,77.6408,560038,Food delivery apps, Direct call,Dinner, Dinner,Veg foods (Breakfast / Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly disagree,Agree,Strongly disagree,Neutral,Agree,Neutral,Strongly disagree,Neutral,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Neutral,Neutral,Neutral,Agree,Strongly disagree,Agree,Maybe,Anytime (Mon-Sun),45 minutes,Neutral,Disagree,Neutral,Neutral,Neutral,Yes,Slightly Important,Very Important,Slightly Important,Slightly Important,Very Important,Very Important,Very Important,Very Important,Yes,Nil
+24,Male,Single,Student,No Income,Post Graduate,3,12.977,77.5773,560009,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Agree,Agree,Agree,Agree,Disagree,Disagree,Neutral,Neutral,Disagree,Disagree,Disagree,Neutral,Neutral,Agree,Disagree,Neutral,Neutral,Yes,Weekend (Sat & Sun),More than 60 minutes,Strongly disagree,Strongly disagree,Disagree,Disagree,Strongly disagree,Yes,Important,Very Important,Important,Important,Very Important,Important,Very Important,Important,Yes,NIL
+22,Male,Single,Student,No Income,Post Graduate,4,13.0298,77.6047,560032,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Strongly agree,Agree,Strongly agree,Agree,Agree,Agree,Agree,Neutral,Disagree,Agree,Strongly agree,Agree,Disagree,Disagree,Agree,Agree,Agree,Strongly agree,Agree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Agree,Strongly Agree,Disagree,Agree,Yes,Very Important,Very Important,Moderately Important,Important,Very Important,Moderately Important,Very Important,Very Important,Yes,Nil
+22,Female,Single,Student,No Income,Graduate,4,12.9983,77.6409,560033,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Agree,Agree,Agree,Agree,Disagree,Strongly agree,Strongly disagree,Agree,Strongly agree,Strongly agree,Strongly disagree,Agree,Disagree,Disagree,Strongly agree,Strongly agree,Strongly disagree,Yes,Weekdays (Mon-Fri),45 minutes,Strongly Agree,Strongly Agree,Strongly Agree,Strongly disagree,Disagree,Yes,Very Important,Important,Slightly Important,Slightly Important,Very Important,Important,Important,Important,Yes,I feel Swiggy has a good interface for users and the delivery time to my place in Bangalore taken by Swiggy is less than Zomato when ordered from same restaurant 
+23,Male,Single,Student,No Income,Graduate,4,12.9925,77.5633,560021,Food delivery apps, Walk-in,Lunch, Dinner,Veg foods (Breakfast / Lunch / Dinner), Ice cream / Cool drinks,Agree,Agree,Agree,Agree,Disagree,Agree,Agree,Neutral,Agree,Agree,Agree,Agree,Agree,Disagree,Neutral,Neutral,Agree,Agree,Agree,Neutral,Yes,Weekdays (Mon-Fri),30 minutes,Neutral,Neutral,Neutral,Neutral,Neutral,Yes,Important,Important,Important,Important,Important,Important,Important,Important,Yes,NIL
+21,Male,Single,Student,Below Rs.10000,Post Graduate,3,12.9306,77.5434,560085,Walk-in, Direct call,Breakfast, Snacks,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Disagree,Agree,Strongly agree,Strongly agree,Agree,Agree,Strongly agree,Strongly agree,Agree,Strongly agree,Disagree,Disagree,Disagree,Neutral,Disagree,Disagree,Disagree,Agree,Agree,No,Anytime (Mon-Sun),45 minutes,Agree,Strongly Agree,Strongly Agree,Strongly Agree,Strongly Agree,Maybe,Moderately Important,Important,Moderately Important,Moderately Important,Moderately Important,Important,Important,Important,Yes,NIL
+25,Male,Single,Student,No Income,Post Graduate,3,12.982,77.6256,560008,Walk-in, Direct call,Dinner, Dinner,Veg foods (Breakfast / Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Agree,Disagree,Neutral,Neutral,Neutral,Agree,Agree,Neutral,Neutral,Agree,Neutral,Agree,Agree,Neutral,Neutral,Disagree,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Neutral,Neutral,Neutral,Neutral,Neutral,Yes,Slightly Important,Slightly Important,Slightly Important,Slightly Important,Slightly Important,Slightly Important,Slightly Important,Slightly Important,Yes,Nil
+22,Female,Single,Student,No Income,Post Graduate,5,12.9353,77.5585,560050,Food delivery apps, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Agree,Agree,Agree,Agree,Disagree,Agree,Disagree,Agree,Neutral,Agree,Neutral,Disagree,Disagree,Disagree,Agree,Agree,Agree,Yes,Anytime (Mon-Sun),45 minutes,Neutral,Agree,Agree,Agree,Agree,Yes,Moderately Important,Important,Important,Important,Important,Important,Important,Important,Yes,NIL
+22,Male,Single,Student,No Income,Post Graduate,3,12.9155,77.5135,560098,Food delivery apps, Walk-in,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Sweets,Agree,Agree,Agree,Agree,Disagree,Neutral,Agree,Disagree,Strongly agree,Neutral,Agree,Neutral,Disagree,Neutral,Disagree,Disagree,Disagree,Strongly agree,Strongly agree,Agree,No,Anytime (Mon-Sun),45 minutes,Disagree,Strongly Agree,Disagree,Disagree,Strongly Agree,Yes,Slightly Important,Important,Very Important,Very Important,Important,Important,Very Important,Very Important,Yes,Nil
+23,Female,Single,Employee,10001 to 25000,Graduate,3,12.9854,77.7081,560048,Walk-in, Direct call,Snacks, Dinner,Bakery items (snacks), Veg foods (Breakfast / Lunch / Dinner),Neutral,Agree,Agree,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Maybe,Weekend (Sat & Sun),30 minutes,Neutral,Neutral,Neutral,Disagree,Neutral,Maybe,Important,Important,Moderately Important,Moderately Important,Important,Important,Important,Important,Yes,NIL
+22,Male,Single,Student,Below Rs.10000,Post Graduate,4,13.0019,77.5713,560003,Walk-in, Direct call,Snacks, Dinner,Veg foods (Breakfast / Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Disagree,Neutral,Neutral,Neutral,Neutral,Neutral,Agree,Neutral,Neutral,Neutral,Agree,Agree,Strongly agree,Strongly agree,Strongly agree,Agree,Yes,Weekend (Sat & Sun),15 minutes,Agree,Neutral,Agree,Agree,Neutral,No,Important,Very Important,Moderately Important,Important,Important,Very Important,Very Important,Very Important,Yes,No
+22,Female,Single,Employee,10001 to 25000,Graduate,5,12.9698,77.75,560066,Food delivery apps, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Agree,Agree,Neutral,Agree,Agree,Neutral,Disagree,Agree,Neutral,Strongly agree,Disagree,Agree,Neutral,Neutral,Agree,Strongly agree,Strongly disagree,Yes,Weekend (Sat & Sun),45 minutes,Strongly Agree,Agree,Agree,Strongly Agree,Disagree,Yes,Important,Important,Important,Very Important,Very Important,Important,Very Important,Very Important,Yes,NIL
+22,Male,Single,Student,No Income,Post Graduate,4,12.9783,77.6408,560038,Food delivery apps, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Neutral,Agree,Agree,Agree,Neutral,Neutral,Neutral,Agree,Agree,Neutral,Neutral,Neutral,Disagree,Agree,Agree,Agree,Agree,Agree,Agree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Agree,Agree,Neutral,Agree,Yes,Moderately Important,Moderately Important,Slightly Important,Moderately Important,Moderately Important,Moderately Important,Moderately Important,Moderately Important,Yes,NIL
+25,Male,Married,Employee,More than 50000,Ph.D,4,12.9261,77.6221,560034,Food delivery apps, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Neutral,Agree,Neutral,Agree,Disagree,Neutral,Agree,Agree,Agree,Disagree,Neutral,Agree,Neutral,Agree,Strongly agree,Strongly disagree,Agree,Agree,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Disagree,Agree,Agree,Agree,Yes,Very Important,Slightly Important,Very Important,Moderately Important,Important,Moderately Important,Very Important,Important,Yes,I expect faster delivery in my area
+22,Female,Single,Student,10001 to 25000,Post Graduate,5,12.985,77.5533,560010,Walk-in, Direct call,Dinner, Dinner,Veg foods (Breakfast / Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Agree,Strongly agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Agree,Neutral,Neutral,Neutral,Agree,Agree,Agree,Neutral,Neutral,Disagree,Yes,Weekend (Sat & Sun),15 minutes,Agree,Agree,Agree,Agree,Agree,Yes,Important,Important,Moderately Important,Important,Very Important,Very Important,Very Important,Very Important,Yes,Nil
+22,Female,Single,Student,No Income,Post Graduate,2,12.9119,77.6446,560102,Food delivery apps, Direct call,Lunch, Snacks,Veg foods (Breakfast / Lunch / Dinner), Ice cream / Cool drinks,Strongly agree,Strongly agree,Agree,Disagree,Disagree,Disagree,Strongly agree,Disagree,Agree,Strongly agree,Disagree,Strongly agree,Disagree,Agree,Strongly agree,Strongly agree,Strongly agree,Neutral,Neutral,Neutral,Yes,Weekend (Sat & Sun),15 minutes,Agree,Agree,Disagree,Agree,Strongly Agree,Yes,Very Important,Very Important,Moderately Important,Important,Very Important,Important,Very Important,Important,Yes,NIL
+25,Male,Single,Student,10001 to 25000,Post Graduate,3,12.9306,77.5434,560085,Food delivery apps, Walk-in,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Agree,Neutral,Neutral,Agree,Strongly disagree,Disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Agree,Agree,Agree,Strongly agree,Agree,Strongly disagree,Yes,Anytime (Mon-Sun),45 minutes,Strongly disagree,Strongly Agree,Strongly Agree,Strongly disagree,Strongly Agree,Yes,Moderately Important,Very Important,Very Important,Moderately Important,Very Important,Very Important,Very Important,Important,Yes,Nil
+25,Male,Single,Student,No Income,Post Graduate,5,12.977,77.5773,560009,Food delivery apps, Walk-in,Lunch, Snacks,Veg foods (Breakfast / Lunch / Dinner), Sweets,Strongly agree,Agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Agree,Strongly agree,Strongly agree,Disagree,Neutral,Disagree,Strongly agree,Neutral,Neutral,Agree,Neutral,Neutral,Neutral,Yes,Anytime (Mon-Sun),45 minutes,Neutral,Agree,Neutral,Disagree,Agree,Maybe,Important,Very Important,Important,Very Important,Very Important,Important,Very Important,Very Important,No,"Now days delivery ?? is improved a lot like fast delivery ,but now corna time many people are hesitating to order "
+32,Female,Prefer not to say,House wife,No Income,Graduate,5,12.982,77.6256,560008,Food delivery apps, Web browser,Breakfast, Lunch,Non Veg foods (Lunch / Dinner), Sweets,Agree,Agree,Agree,Neutral,Disagree,Agree,Agree,Agree,Disagree,Agree,Neutral,Agree,Agree,Agree,Agree,Disagree,Agree,Agree,Neutral,Agree,Yes,Weekdays (Mon-Fri),60 minutes,Agree,Agree,Strongly disagree,Agree,Neutral,Yes,Important,Important,Slightly Important,Important,Moderately Important,Important,Slightly Important,Important,Yes,I love zomato but it requires quality in deliver service. 
+23,Female,Single,Student,No Income,Post Graduate,3,12.9438,77.5738,560004,Food delivery apps, Walk-in,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Agree,Agree,Neutral,Disagree,Agree,Strongly agree,Agree,Neutral,Agree,Agree,Disagree,Agree,Neutral,Agree,Agree,Strongly agree,Strongly agree,Yes,Weekend (Sat & Sun),45 minutes,Neutral,Agree,Agree,Disagree,Agree,Yes,Very Important,Very Important,Moderately Important,Important,Very Important,Important,Very Important,Very Important,Yes,NIL
+23,Female,Single,Student,No Income,Post Graduate,4,12.8988,77.5764,560078,Walk-in, Direct call,Breakfast, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Agree,Agree,Neutral,Neutral,Agree,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Agree,Agree,Neutral,Neutral,Agree,Neutral,Yes,Weekend (Sat & Sun),45 minutes,Agree,Neutral,Agree,Neutral,Neutral,Yes,Moderately Important,Moderately Important,Slightly Important,Moderately Important,Moderately Important,Moderately Important,Moderately Important,Moderately Important,Yes,NIL
+30,Male,Married,Self Employeed,More than 50000,Uneducated,4,12.9662,77.6068,560025,Food delivery apps, Web browser,Lunch, Snacks,Veg foods (Breakfast / Lunch / Dinner), Sweets,Agree,Strongly agree,Agree,Disagree,Neutral,Agree,Neutral,Agree,Strongly agree,Agree,Strongly agree,Strongly agree,Neutral,Strongly agree,Agree,Strongly agree,Agree,Neutral,Agree,Strongly agree,Yes,Weekdays (Mon-Fri),More than 60 minutes,Agree,Strongly Agree,Strongly Agree,Neutral,Agree,Yes,Important,Important,Moderately Important,Important,Important,Moderately Important,Important,Important,Yes,Prefer hygienic delivery people
+23,Male,Single,Student,No Income,Graduate,3,12.9565,77.5484,560026,Food delivery apps, Walk-in,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Strongly agree,Strongly agree,Agree,Agree,Agree,Agree,Agree,Agree,Neutral,Neutral,Strongly agree,Neutral,Strongly agree,Agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Yes,Weekend (Sat & Sun),30 minutes,Agree,Agree,Neutral,Neutral,Strongly Agree,Yes,Very Important,Very Important,Slightly Important,Very Important,Very Important,Important,Very Important,Very Important,Yes,nil
+23,Male,Single,Student,No Income,Post Graduate,4,12.9925,77.5633,560021,Food delivery apps, Direct call,Lunch, Dinner,Veg foods (Breakfast / Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Disagree,Agree,Agree,Agree,Neutral,Neutral,Strongly agree,Strongly agree,Disagree,Agree,Disagree,Disagree,Strongly agree,Agree,Agree,Agree,Strongly agree,Strongly agree,Agree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Agree,Agree,Neutral,Strongly Agree,Yes,Important,Important,Moderately Important,Important,Very Important,Important,Important,Important,Yes,NIL
+22,Female,Single,Student,No Income,Post Graduate,5,12.985,77.5533,560010,Walk-in, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Bakery items (snacks),Agree,Agree,Agree,Agree,Disagree,Neutral,Agree,Strongly agree,Strongly agree,Neutral,Agree,Neutral,Agree,Agree,Agree,Agree,Neutral,Agree,Disagree,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Strongly Agree,Strongly disagree,Agree,Yes,Important,Very Important,Important,Very Important,Very Important,Very Important,Very Important,Very Important,Yes,NIL
+22,Male,Single,Student,No Income,Graduate,5,12.985,77.5533,560010,Food delivery apps, Walk-in,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Ice cream / Cool drinks,Agree,Agree,Agree,Agree,Agree,Disagree,Neutral,Disagree,Disagree,Agree,Agree,Agree,Disagree,Disagree,Agree,Disagree,Agree,Agree,Agree,Agree,Yes,Anytime (Mon-Sun),30 minutes,Neutral,Agree,Agree,Disagree,Agree,Yes,Important,Important,Slightly Important,Moderately Important,Very Important,Important,Important,Important,Yes,NIL
+27,Female,Married,Self Employeed,10001 to 25000,Post Graduate,2,12.9261,77.6221,560034,Food delivery apps, Direct call,Breakfast, Lunch,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Agree,Strongly agree,Strongly agree,Agree,Neutral,Disagree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Agree,Neutral,Strongly agree,Strongly agree,Agree,Strongly agree,Neutral,Disagree,Agree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Agree,Strongly Agree,Agree,No,Very Important,Moderately Important,Important,Moderately Important,Very Important,Very Important,Important,Moderately Important,Yes,NIL
+24,Female,Single,Student,No Income,Post Graduate,3,12.977,77.5773,560009,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Strongly agree,Agree,Neutral,Agree,Neutral,Agree,Neutral,Strongly agree,Neutral,Strongly agree,Agree,Agree,Neutral,Agree,Neutral,Neutral,Strongly agree,Strongly agree,Strongly agree,Maybe,Anytime (Mon-Sun),45 minutes,Agree,Strongly Agree,Agree,Disagree,Agree,Yes,Moderately Important,Very Important,Unimportant,Important,Very Important,Important,Very Important,Very Important,Yes,NIL
+23,Male,Single,Student,No Income,Post Graduate,2,12.977,77.5773,560009,Food delivery apps, Walk-in,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Neutral,Disagree,Neutral,Disagree,Neutral,Neutral,Agree,Neutral,Disagree,Disagree,Agree,Neutral,Neutral,Neutral,Agree,Neutral,Disagree,Neutral,Neutral,Disagree,Yes,Weekend (Sat & Sun),30 minutes,Neutral,Disagree,Neutral,Disagree,Neutral,Yes,Moderately Important,Slightly Important,Moderately Important,Slightly Important,Slightly Important,Unimportant,Slightly Important,Slightly Important,Yes,-
+23,Female,Single,Student,No Income,Graduate,3,12.982,77.6256,560008,Food delivery apps, Walk-in,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Disagree,Disagree,Disagree,Agree,Neutral,Neutral,Neutral,Strongly agree,Neutral,Disagree,Strongly disagree,Disagree,Disagree,Disagree,Agree,Disagree,Agree,Yes,Anytime (Mon-Sun),45 minutes,Disagree,Agree,Neutral,Agree,Neutral,Yes,Important,Very Important,Slightly Important,Important,Very Important,Very Important,Very Important,Important,Yes,"Spillage, bad packaging and missing items "
+22,Female,Single,Student,10001 to 25000,Post Graduate,5,12.985,77.5533,560010,Walk-in, Direct call,Dinner, Dinner,Veg foods (Breakfast / Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Agree,Strongly agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Agree,Neutral,Neutral,Neutral,Agree,Agree,Agree,Neutral,Neutral,Disagree,Yes,Weekend (Sat & Sun),15 minutes,Agree,Agree,Agree,Agree,Agree,Yes,Important,Important,Moderately Important,Important,Very Important,Very Important,Very Important,Very Important,Yes,Nil
+23,Female,Single,Student,No Income,Graduate,5,13.0206,77.6479,560043,Food delivery apps, Direct call,Lunch, Snacks,Veg foods (Breakfast / Lunch / Dinner), Ice cream / Cool drinks,Strongly agree,Agree,Agree,Neutral,Agree,Neutral,Neutral,Agree,Neutral,Agree,Agree,Neutral,Agree,Disagree,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Yes,Weekdays (Mon-Fri),45 minutes,Neutral,Neutral,Neutral,Neutral,Neutral,Yes,Slightly Important,Moderately Important,Slightly Important,Slightly Important,Important,Important,Important,Important,Yes,Nil
+23,Female,Single,Student,No Income,Post Graduate,2,12.977,77.5773,560009,Food delivery apps, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Maybe,Anytime (Mon-Sun),45 minutes,Neutral,Neutral,Neutral,Neutral,Neutral,Maybe,Important,Important,Unimportant,Important,Very Important,Very Important,Very Important,Very Important,Yes,NiL
+24,Male,Single,Student,No Income,Post Graduate,3,12.977,77.5773,560009,Food delivery apps, Walk-in,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Ice cream / Cool drinks,Agree,Strongly agree,Neutral,Strongly agree,Agree,Neutral,Neutral,Disagree,Agree,Agree,Strongly agree,Agree,Agree,Disagree,Strongly agree,Strongly agree,Agree,Strongly agree,Strongly agree,Strongly disagree,Yes,Weekend (Sat & Sun),30 minutes,Strongly Agree,Agree,Agree,Agree,Agree,Yes,Slightly Important,Important,Important,Important,Very Important,Important,Very Important,Important,Yes,No
+25,Male,Single,Student,No Income,Post Graduate,2,12.9635,77.5821,560002,Walk-in, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Agree,Agree,Agree,Agree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Agree,Agree,Agree,Strongly disagree,Strongly disagree,Strongly disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Agree,Agree,Agree,Agree,Yes,Moderately Important,Moderately Important,Moderately Important,Moderately Important,Moderately Important,Moderately Important,Moderately Important,Moderately Important,Yes,N0
+22,Male,Single,Student,No Income,Post Graduate,3,12.9306,77.5434,560085,Food delivery apps, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Sweets,Agree,Agree,Strongly agree,Agree,Agree,Agree,Strongly agree,Neutral,Neutral,Disagree,Neutral,Disagree,Disagree,Disagree,Agree,Neutral,Neutral,Agree,Agree,Strongly agree,Yes,Weekend (Sat & Sun),45 minutes,Agree,Agree,Neutral,Neutral,Agree,Yes,Important,Important,Important,Important,Important,Important,Important,Important,Yes,Nil
+28,Female,Married,Student,No Income,Graduate,2,13.0067,77.545,560086,Food delivery apps, Direct call,Lunch, Dinner,Veg foods (Breakfast / Lunch / Dinner), Sweets,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Neutral,Disagree,Strongly agree,Agree,Strongly agree,Agree,Strongly agree,Strongly agree,Disagree,Strongly agree,Neutral,Strongly agree,Yes,Anytime (Mon-Sun),30 minutes,Disagree,Neutral,Disagree,Neutral,Strongly Agree,Yes,Very Important,Very Important,Moderately Important,Very Important,Very Important,Moderately Important,Very Important,Moderately Important,Yes,I have normally had good experiences with swiggy especially for payments and refunds
+22,Female,Single,Student,No Income,Post Graduate,1,12.8845,77.6036,560076,Food delivery apps, Walk-in,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Neutral,Neutral,Agree,Neutral,Neutral,Strongly agree,Agree,Neutral,Agree,Neutral,Neutral,Neutral,Neutral,Neutral,Agree,Agree,Agree,Agree,Maybe,Anytime (Mon-Sun),60 minutes,Agree,Agree,Strongly Agree,Neutral,Neutral,Yes,Very Important,Important,Moderately Important,Important,Very Important,Important,Very Important,Very Important,Yes,NIL
+24,Female,Single,Student,No Income,Graduate,3,12.977,77.5773,560009,Food delivery apps, Direct call,Lunch, Snacks,Non Veg foods (Lunch / Dinner), Bakery items (snacks),Strongly agree,Agree,Strongly agree,Strongly agree,Agree,Neutral,Neutral,Agree,Agree,Strongly agree,Agree,Strongly agree,Neutral,Agree,Agree,Strongly agree,Agree,Agree,Agree,Agree,Yes,Weekend (Sat & Sun),60 minutes,Agree,Strongly Agree,Agree,Neutral,Agree,Yes,Slightly Important,Moderately Important,Unimportant,Slightly Important,Very Important,Very Important,Important,Very Important,Yes,NIL
+31,Male,Married,Employee,More than 50000,Ph.D,5,12.9119,77.6446,560102,Walk-in, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Agree,Neutral,Neutral,Disagree,Neutral,Neutral,Strongly agree,Agree,Neutral,Neutral,Disagree,Disagree,Strongly disagree,Agree,Agree,Agree,Neutral,Neutral,Disagree,Yes,Weekend (Sat & Sun),45 minutes,Agree,Neutral,Disagree,Agree,Agree,Maybe,Important,Important,Moderately Important,Important,Important,Important,Important,Important,Yes,NIL
+25,Male,Single,Student,No Income,Post Graduate,4,13.0067,77.545,560086,Food delivery apps, Walk-in,Snacks, Dinner,Veg foods (Breakfast / Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Neutral,Strongly agree,Strongly agree,Strongly agree,Neutral,Agree,Disagree,Neutral,Strongly agree,Neutral,Disagree,Disagree,Agree,Neutral,Strongly agree,Agree,Strongly agree,Strongly agree,Agree,Yes,Anytime (Mon-Sun),30 minutes,Neutral,Strongly Agree,Strongly Agree,Strongly Agree,Strongly Agree,Yes,Important,Very Important,Important,Important,Very Important,Very Important,Very Important,Very Important,Yes,NIL
+23,Male,Single,Student,No Income,Post Graduate,5,12.8988,77.5764,560078,Food delivery apps, Walk-in,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Ice cream / Cool drinks,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Agree,Agree,Neutral,Neutral,Neutral,Strongly agree,Strongly agree,Strongly agree,Agree,Disagree,Disagree,Disagree,Strongly agree,Strongly agree,Agree,Maybe,Weekend (Sat & Sun),60 minutes,Agree,Agree,Agree,Disagree,Strongly Agree,Maybe,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Yes,Nil
+22,Male,Single,Student,No Income,Post Graduate,3,12.8845,77.6036,560076,Food delivery apps, Walk-in,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Neutral,Strongly agree,Strongly agree,Agree,Neutral,Agree,Agree,Neutral,Agree,Agree,Neutral,Strongly disagree,Neutral,Agree,Neutral,Neutral,Agree,Disagree,Agree,Maybe,Anytime (Mon-Sun),30 minutes,Strongly Agree,Agree,Neutral,Agree,Neutral,No,Very Important,Very Important,Important,Important,Very Important,Important,Very Important,Very Important,Yes,Nill
+23,Male,Single,Student,25001 to 50000,Post Graduate,1,13.0158,77.539,560096,Food delivery apps, Walk-in,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Neutral,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Neutral,Strongly disagree,Strongly disagree,Neutral,Neutral,Neutral,Neutral,Neutral,Disagree,Neutral,Neutral,Strongly disagree,Strongly disagree,Neutral,No,Weekend (Sat & Sun),60 minutes,Disagree,Disagree,Neutral,Strongly disagree,Strongly disagree,Yes,Very Important,Very Important,Slightly Important,Very Important,Very Important,Very Important,Very Important,Very Important,Yes,NIL
+23,Male,Single,Student,No Income,Graduate,4,12.9343,77.6044,560029,Food delivery apps, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Ice cream / Cool drinks,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly disagree,Neutral,Agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Neutral,Agree,Agree,Strongly agree,Strongly agree,Strongly agree,Disagree,Maybe,Weekend (Sat & Sun),30 minutes,Strongly Agree,Strongly Agree,Strongly Agree,Disagree,Neutral,Yes,Very Important,Very Important,Moderately Important,Very Important,Very Important,Very Important,Very Important,Very Important,Yes,Nil
+23,Female,Single,Student,No Income,Post Graduate,2,13.0019,77.5713,560003,Food delivery apps, Walk-in,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Neutral,Agree,Strongly agree,Strongly agree,Agree,Strongly agree,Neutral,Agree,Strongly disagree,Strongly agree,Strongly agree,Agree,Disagree,Strongly agree,Neutral,Maybe,Anytime (Mon-Sun),45 minutes,Disagree,Strongly disagree,Neutral,Strongly disagree,Strongly Agree,Yes,Moderately Important,Moderately Important,Very Important,Very Important,Very Important,Important,Very Important,Moderately Important,Yes,NIL
+25,Male,Single,Student,No Income,Post Graduate,6,13.0012,77.5995,560046,Food delivery apps, Walk-in,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Ice cream / Cool drinks,Agree,Neutral,Agree,Agree,Neutral,Neutral,Agree,Agree,Agree,Strongly agree,Agree,Neutral,Agree,Agree,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Yes,Weekend (Sat & Sun),60 minutes,Neutral,Neutral,Neutral,Neutral,Neutral,Yes,Important,Important,Important,Important,Important,Important,Important,Important,Yes,No
+24,Male,Single,Employee,10001 to 25000,Graduate,4,12.9442,77.6076,560030,Food delivery apps, Web browser,Lunch, Snacks,Non Veg foods (Lunch / Dinner), Bakery items (snacks),Neutral,Neutral,Disagree,Neutral,Disagree,Strongly disagree,Strongly disagree,Disagree,Neutral,Disagree,Disagree,Strongly disagree,Disagree,Neutral,Disagree,Strongly disagree,Neutral,Neutral,Disagree,Neutral,Maybe,Anytime (Mon-Sun),30 minutes,Strongly disagree,Disagree,Neutral,Disagree,Disagree,Yes,Slightly Important,Important,Important,Moderately Important,Moderately Important,Very Important,Very Important,Very Important,Yes,Good
+23,Female,Single,Student,No Income,Post Graduate,4,13.0487,77.5923,560024,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Strongly agree,Strongly agree,Neutral,Neutral,Strongly agree,Strongly agree,Neutral,Disagree,Neutral,Strongly agree,Agree,Neutral,Disagree,Agree,Agree,Agree,Strongly agree,Agree,Neutral,Yes,Weekend (Sat & Sun),45 minutes,Neutral,Neutral,Neutral,Neutral,Neutral,Yes,Important,Important,Moderately Important,Moderately Important,Very Important,Very Important,Very Important,Very Important,Yes,Nil
+23,Female,Single,Student,No Income,Post Graduate,4,13.0487,77.5923,560024,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Strongly agree,Strongly agree,Neutral,Neutral,Strongly agree,Strongly agree,Neutral,Disagree,Neutral,Strongly agree,Agree,Neutral,Disagree,Agree,Agree,Agree,Strongly agree,Agree,Neutral,Yes,Weekend (Sat & Sun),45 minutes,Neutral,Neutral,Neutral,Neutral,Neutral,Yes,Important,Important,Moderately Important,Moderately Important,Very Important,Very Important,Very Important,Very Important,Yes,Nil
+24,Female,Married,Employee,More than 50000,Ph.D,4,12.9438,77.5738,560004,Food delivery apps, Direct call,Dinner, Dinner,Veg foods (Breakfast / Lunch / Dinner), Sweets,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Strongly agree,Agree,Neutral,Disagree,Disagree,Agree,Neutral,Disagree,Strongly agree,Agree,Yes,Weekend (Sat & Sun),30 minutes,Agree,Neutral,Agree,Strongly disagree,Agree,Yes,Important,Slightly Important,Moderately Important,Very Important,Very Important,Very Important,Very Important,Important,Yes,Good so far
+22,Male,Single,Student,No Income,Graduate,4,12.9889,77.5741,560020,Food delivery apps, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Sweets,Disagree,Strongly disagree,Agree,Neutral,Disagree,Disagree,Agree,Disagree,Neutral,Agree,Neutral,Agree,Neutral,Strongly agree,Neutral,Agree,Neutral,Strongly disagree,Neutral,Strongly disagree,Yes,Anytime (Mon-Sun),30 minutes,Strongly Agree,Strongly Agree,Disagree,Strongly disagree,Strongly Agree,Yes,Very Important,Very Important,Unimportant,Slightly Important,Very Important,Moderately Important,Very Important,Important,Yes,Nil
+24,Female,Single,Student,10001 to 25000,Post Graduate,3,12.9335,77.5691,560028,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Sweets,Agree,Neutral,Strongly agree,Strongly agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Neutral,Neutral,Disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Agree,Neutral,Strongly disagree,Yes,Anytime (Mon-Sun),45 minutes,Strongly Agree,Strongly Agree,Strongly Agree,Disagree,Agree,Yes,Very Important,Very Important,Important,Very Important,Very Important,Very Important,Very Important,Very Important,No,Nil
+25,Female,Single,Student,No Income,Post Graduate,3,12.9766,77.5993,560001,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Neutral,Strongly agree,Disagree,Neutral,Disagree,Neutral,Agree,Agree,Neutral,Neutral,Disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Strongly agree,Strongly agree,Strongly agree,No,Weekend (Sat & Sun),45 minutes,Neutral,Disagree,Neutral,Disagree,Neutral,No,Moderately Important,Very Important,Important,Important,Very Important,Important,Very Important,Very Important,Yes,NIL
+23,Male,Single,Student,No Income,Post Graduate,2,12.8845,77.6036,560076,Food delivery apps, Direct call,Breakfast, Lunch,Veg foods (Breakfast / Lunch / Dinner), Bakery items (snacks),Agree,Agree,Strongly agree,Strongly agree,Strongly agree,Neutral,Neutral,Disagree,Neutral,Agree,Strongly agree,Agree,Agree,Agree,Agree,Agree,Agree,Neutral,Agree,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Agree,Agree,Agree,Agree,No,Important,Very Important,Slightly Important,Important,Very Important,Very Important,Very Important,Very Important,Yes,Nil
+26,Male,Single,Student,No Income,Post Graduate,4,13.0019,77.5713,560003,Walk-in, Direct call,Dinner, Dinner,Veg foods (Breakfast / Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Neutral,Strongly agree,Strongly disagree,Neutral,Agree,Neutral,Strongly disagree,Agree,Strongly disagree,Strongly agree,Agree,Agree,Strongly agree,Strongly agree,Neutral,Yes,Anytime (Mon-Sun),30 minutes,Neutral,Neutral,Strongly Agree,Agree,Neutral,No,Important,Very Important,Moderately Important,Very Important,Very Important,Very Important,Very Important,Very Important,Yes,Nil
+24,Female,Single,Student,25001 to 50000,Post Graduate,3,13.102,77.5864,560064,Walk-in, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Neutral,Strongly agree,Neutral,Neutral,Strongly agree,Agree,Strongly agree,Neutral,Agree,Disagree,Agree,Agree,Disagree,Disagree,Disagree,Strongly disagree,Strongly disagree,Strongly disagree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Strongly Agree,Strongly Agree,Strongly disagree,Strongly Agree,Yes,Important,Very Important,Very Important,Very Important,Very Important,Important,Very Important,Very Important,Yes,Nil
+26,Male,Single,Student,No Income,Post Graduate,4,12.9048,77.6821,560036,Food delivery apps, Direct call,Lunch, Snacks,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Strongly agree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Disagree,Neutral,Agree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Agree,Agree,Neutral,Strongly Agree,Yes,Important,Very Important,Slightly Important,Important,Very Important,Very Important,Very Important,Very Important,Yes,NIL
+21,Male,Single,Student,No Income,Graduate,4,12.977,77.5773,560009,Walk-in, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Disagree,Strongly agree,Neutral,Neutral,Neutral,Disagree,Disagree,Agree,Agree,Agree,Strongly disagree,Disagree,Strongly disagree,No,Weekend (Sat & Sun),30 minutes,Agree,Neutral,Disagree,Strongly Agree,Agree,Yes,Important,Important,Slightly Important,Moderately Important,Very Important,Very Important,Very Important,Very Important,Yes,Nil
+22,Female,Single,Student,No Income,Post Graduate,3,12.977,77.5773,560009,Food delivery apps, Walk-in,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Ice cream / Cool drinks,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Neutral,Neutral,Strongly disagree,Neutral,Neutral,Neutral,Neutral,Strongly agree,Strongly agree,Neutral,Neutral,Neutral,Neutral,Yes,Weekend (Sat & Sun),30 minutes,Strongly Agree,Strongly Agree,Neutral,Strongly Agree,Strongly Agree,Yes,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Yes,NIL
+24,Male,Single,Student,No Income,Post Graduate,5,12.9337,77.59,560011,Food delivery apps, Direct call,Snacks, Dinner,Veg foods (Breakfast / Lunch / Dinner), Bakery items (snacks),Agree,Agree,Agree,Agree,Agree,Neutral,Neutral,Neutral,Neutral,Neutral,Agree,Agree,Agree,Strongly disagree,Agree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,No,Anytime (Mon-Sun),More than 60 minutes,Neutral,Neutral,Agree,Strongly disagree,Agree,Maybe,Unimportant,Slightly Important,Unimportant,Very Important,Slightly Important,Slightly Important,Slightly Important,Slightly Important,Yes,NIL
+24,Male,Single,Student,10001 to 25000,Post Graduate,4,12.9037,77.5376,560061,Food delivery apps, Walk-in,Lunch, Snacks,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Strongly agree,Strongly agree,Strongly agree,Agree,Agree,Agree,Agree,Agree,Neutral,Agree,Agree,Disagree,Disagree,Agree,Agree,Agree,Agree,Neutral,Strongly disagree,Yes,Anytime (Mon-Sun),45 minutes,Neutral,Strongly disagree,Strongly Agree,Agree,Agree,Yes,Important,Important,Moderately Important,Very Important,Very Important,Important,Very Important,Very Important,Yes,Nil
+23,Female,Single,Student,No Income,Post Graduate,3,12.977,77.5773,560009,Food delivery apps, Walk-in,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Ice cream / Cool drinks,Neutral,Agree,Strongly agree,Strongly agree,Disagree,Strongly agree,Strongly agree,Disagree,Agree,Neutral,Neutral,Disagree,Agree,Neutral,Strongly agree,Neutral,Agree,Disagree,Neutral,Disagree,Yes,Weekend (Sat & Sun),45 minutes,Agree,Strongly Agree,Strongly Agree,Strongly disagree,Strongly Agree,Yes,Important,Very Important,Unimportant,Moderately Important,Important,Very Important,Very Important,Important,Yes,NIL
+23,Male,Single,Student,No Income,Post Graduate,3,12.9343,77.6044,560029,Food delivery apps, Direct call,Lunch, Dinner,Veg foods (Breakfast / Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Strongly disagree,Strongly disagree,Strongly disagree,Disagree,Strongly disagree,Disagree,Disagree,Strongly disagree,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Yes,Weekend (Sat & Sun),45 minutes,Neutral,Neutral,Neutral,Strongly Agree,Neutral,Maybe,Very Important,Very Important,Slightly Important,Very Important,Very Important,Important,Very Important,Very Important,Yes,Nil
+22,Male,Single,Student,No Income,Post Graduate,3,12.9438,77.5738,560004,Food delivery apps, Direct call,Breakfast, Lunch,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Agree,Neutral,Neutral,Strongly agree,Neutral,Agree,Neutral,Neutral,Neutral,Neutral,Agree,Neutral,Disagree,Agree,Neutral,Neutral,Agree,Agree,Neutral,Yes,Weekend (Sat & Sun),60 minutes,Agree,Neutral,Neutral,Neutral,Neutral,Yes,Important,Very Important,Unimportant,Moderately Important,Very Important,Moderately Important,Important,Very Important,Yes,NIL
+23,Male,Single,Student,No Income,Post Graduate,3,12.977,77.5773,560009,Food delivery apps, Direct call,Dinner, Dinner,Veg foods (Breakfast / Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Neutral,Agree,Strongly agree,Strongly agree,Neutral,Agree,Neutral,Disagree,Agree,Agree,Agree,Strongly agree,Neutral,Agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Disagree,Yes,Weekend (Sat & Sun),30 minutes,Agree,Agree,Agree,Disagree,Disagree,Yes,Important,Very Important,Moderately Important,Moderately Important,Important,Very Important,Very Important,Very Important,Yes,NIL
+24,Female,Single,Student,No Income,Post Graduate,4,12.9783,77.6408,560038,Food delivery apps, Walk-in,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Ice cream / Cool drinks,Strongly agree,Neutral,Agree,Agree,Agree,Neutral,Strongly agree,Neutral,Disagree,Neutral,Disagree,Neutral,Neutral,Disagree,Neutral,Neutral,Neutral,Neutral,Disagree,Disagree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Agree,Neutral,Disagree,Agree,Maybe,Very Important,Very Important,Slightly Important,Important,Very Important,Important,Very Important,Very Important,Yes,Nil
+24,Male,Single,Student,No Income,Post Graduate,5,12.9337,77.59,560011,Food delivery apps, Direct call,Snacks, Dinner,Veg foods (Breakfast / Lunch / Dinner), Bakery items (snacks),Agree,Agree,Agree,Agree,Agree,Neutral,Neutral,Neutral,Neutral,Neutral,Agree,Agree,Agree,Strongly disagree,Agree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,No,Anytime (Mon-Sun),More than 60 minutes,Neutral,Neutral,Agree,Strongly disagree,Agree,Maybe,Unimportant,Slightly Important,Unimportant,Very Important,Slightly Important,Slightly Important,Slightly Important,Slightly Important,Yes,NIL
+25,Male,Single,Student,No Income,Graduate,1,12.977,77.5773,560009,Food delivery apps, Walk-in,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Neutral,Strongly agree,Agree,Strongly agree,Neutral,Agree,Strongly agree,Strongly agree,Strongly disagree,Strongly disagree,Disagree,Neutral,Strongly disagree,Neutral,Strongly disagree,Strongly disagree,Neutral,Neutral,Neutral,Yes,Anytime (Mon-Sun),45 minutes,Neutral,Agree,Strongly disagree,Neutral,Agree,Yes,Important,Important,Important,Very Important,Very Important,Very Important,Very Important,Very Important,Yes,NIL
+25,Male,Single,Student,No Income,Post Graduate,5,12.977,77.5773,560009,Food delivery apps, Walk-in,Lunch, Snacks,Veg foods (Breakfast / Lunch / Dinner), Sweets,Strongly agree,Agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Agree,Strongly agree,Strongly agree,Disagree,Neutral,Disagree,Strongly agree,Neutral,Neutral,Agree,Neutral,Neutral,Neutral,Yes,Anytime (Mon-Sun),45 minutes,Neutral,Agree,Neutral,Disagree,Agree,Maybe,Important,Very Important,Important,Very Important,Very Important,Important,Very Important,Very Important,No,"Now days delivery ?? is improved a lot like fast delivery ,but now corna time many people are hesitating to order "
+28,Male,Married,Self Employeed,10001 to 25000,Graduate,2,13.0289,77.54,560022,Food delivery apps, Direct call,Breakfast, Dinner,Non Veg foods (Lunch / Dinner), Ice cream / Cool drinks,Disagree,Neutral,Disagree,Disagree,Disagree,Strongly disagree,Neutral,Strongly disagree,Disagree,Neutral,Strongly disagree,Disagree,Disagree,Neutral,Agree,Agree,Neutral,Agree,Neutral,Agree,No,Anytime (Mon-Sun),60 minutes,Agree,Neutral,Neutral,Disagree,Neutral,No,Important,Moderately Important,Slightly Important,Slightly Important,Important,Slightly Important,Important,Slightly Important,No,I hate online deliveries.Had harsh past experiences with delivery boys and the company responses
+27,Female,Prefer not to say,Employee,25001 to 50000,Post Graduate,5,13.0289,77.54,560022,Food delivery apps, Web browser,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Bakery items (snacks),Agree,Agree,Disagree,Strongly agree,Strongly disagree,Disagree,Strongly agree,Agree,Agree,Agree,Strongly agree,Disagree,Agree,Agree,Agree,Strongly agree,Neutral,Agree,Agree,Neutral,No,Weekdays (Mon-Fri),45 minutes,Agree,Strongly Agree,Agree,Neutral,Agree,No,Slightly Important,Very Important,Slightly Important,Important,Slightly Important,Very Important,Important,Important,No,nil
+26,Male,Single,Self Employeed,10001 to 25000,Ph.D,1,12.9698,77.75,560066,Food delivery apps, Web browser,Lunch, Snacks,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Disagree,Agree,Neutral,Disagree,Neutral,Agree,Neutral,Agree,Agree,Agree,Agree,Strongly agree,Neutral,Agree,Agree,Neutral,Disagree,Strongly disagree,Yes,Weekend (Sat & Sun),More than 60 minutes,Agree,Agree,Neutral,Neutral,Disagree,Yes,Moderately Important,Important,Important,Important,Important,Very Important,Important,Slightly Important,No,I expect the food that is being delivered to be hot and fresh
+22,Male,Single,Student,No Income,Post Graduate,2,12.977,77.5773,560009,Food delivery apps, Walk-in,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Strongly agree,Neutral,Strongly agree,Agree,Strongly agree,Strongly agree,Strongly agree,Agree,Neutral,Agree,Strongly agree,Agree,Neutral,Agree,Strongly agree,Neutral,Agree,Yes,Weekdays (Mon-Fri),15 minutes,Agree,Strongly Agree,Agree,Neutral,Agree,Yes,Very Important,Important,Very Important,Important,Moderately Important,Important,Important,Moderately Important,Yes,I love swiggy and their app user interface
+24,Female,Single,Student,No Income,Post Graduate,3,12.977,77.5773,560009,Food delivery apps, Walk-in,Breakfast, Lunch,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Agree,Neutral,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Yes,Anytime (Mon-Sun),30 minutes,Strongly Agree,Strongly Agree,Neutral,Strongly disagree,Strongly Agree,Yes,Very Important,Very Important,Very Important,Slightly Important,Very Important,Very Important,Very Important,Very Important,Yes,Nil
+23,Male,Single,Student,No Income,Post Graduate,1,12.9561,77.5921,560027,Walk-in, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Neutral,Neutral,Agree,Disagree,Disagree,Neutral,Disagree,Neutral,Strongly agree,Strongly agree,Agree,Strongly agree,Neutral,Disagree,Strongly agree,Agree,Neutral,Neutral,Neutral,Disagree,Yes,Weekend (Sat & Sun),30 minutes,Agree,Disagree,Agree,Disagree,Agree,Yes,Important,Important,Slightly Important,Moderately Important,Important,Moderately Important,Important,Important,Yes,No Comments!
+25,Male,Single,Student,No Income,Graduate,1,12.977,77.5773,560009,Food delivery apps, Walk-in,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Neutral,Strongly agree,Agree,Strongly agree,Neutral,Agree,Strongly agree,Strongly agree,Strongly disagree,Strongly disagree,Disagree,Neutral,Strongly disagree,Neutral,Strongly disagree,Strongly disagree,Neutral,Neutral,Neutral,Yes,Anytime (Mon-Sun),45 minutes,Neutral,Agree,Strongly disagree,Neutral,Agree,Yes,Important,Important,Important,Very Important,Very Important,Very Important,Very Important,Very Important,Yes,NIL
+23,Female,Single,Student,No Income,Graduate,5,13.0206,77.6479,560043,Food delivery apps, Direct call,Lunch, Snacks,Veg foods (Breakfast / Lunch / Dinner), Ice cream / Cool drinks,Strongly agree,Agree,Agree,Neutral,Agree,Neutral,Neutral,Agree,Neutral,Agree,Agree,Neutral,Agree,Disagree,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Yes,Weekdays (Mon-Fri),45 minutes,Neutral,Neutral,Neutral,Neutral,Neutral,Yes,Slightly Important,Moderately Important,Slightly Important,Slightly Important,Important,Important,Important,Important,Yes,Nil
+23,Female,Single,Student,No Income,Graduate,5,13.0206,77.6479,560043,Food delivery apps, Direct call,Lunch, Snacks,Veg foods (Breakfast / Lunch / Dinner), Ice cream / Cool drinks,Strongly agree,Agree,Agree,Neutral,Agree,Neutral,Neutral,Agree,Neutral,Agree,Agree,Neutral,Agree,Disagree,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Yes,Weekdays (Mon-Fri),45 minutes,Neutral,Neutral,Neutral,Neutral,Neutral,Yes,Slightly Important,Moderately Important,Slightly Important,Slightly Important,Important,Important,Important,Important,Yes,Nil
+26,Male,Married,Employee,25001 to 50000,Graduate,5,12.9579,77.6309,560007,Food delivery apps, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Strongly agree,Strongly agree,Neutral,Agree,Strongly agree,Agree,Neutral,Neutral,Neutral,Disagree,Disagree,Agree,Agree,Agree,Neutral,Agree,Neutral,Disagree,No,Weekend (Sat & Sun),60 minutes,Agree,Agree,Neutral,Agree,Agree,No,Important,Important,Moderately Important,Important,Slightly Important,Slightly Important,Slightly Important,Important,No,I expect quality food rather than delivery time and ratings
+32,Female,Married,House wife,No Income,Uneducated,3,13.014,77.5658,560012,Food delivery apps, Direct call,Dinner, Dinner,Veg foods (Breakfast / Lunch / Dinner), Sweets,Strongly disagree,Disagree,Disagree,Disagree,Disagree,Strongly disagree,Disagree,Strongly agree,Strongly agree,Strongly agree,Strongly disagree,Strongly agree,Disagree,Strongly agree,Agree,Neutral,Agree,Agree,Neutral,Agree,Maybe,Anytime (Mon-Sun),30 minutes,Agree,Neutral,Agree,Agree,Neutral,Yes,Important,Important,Important,Very Important,Important,Very Important,Important,Slightly Important,No,Expect the delivery person to understand our hunger
+24,Female,Single,Student,10001 to 25000,Post Graduate,3,12.9335,77.5691,560028,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Sweets,Agree,Neutral,Strongly agree,Strongly agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Neutral,Neutral,Disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Agree,Neutral,Strongly disagree,Yes,Anytime (Mon-Sun),45 minutes,Strongly Agree,Strongly Agree,Strongly Agree,Disagree,Agree,Yes,Very Important,Very Important,Important,Very Important,Very Important,Very Important,Very Important,Very Important,No,Nil
+23,Male,Single,Student,No Income,Post Graduate,2,12.9442,77.6076,560030,Food delivery apps, Walk-in,Breakfast, Dinner,Veg foods (Breakfast / Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Strongly agree,Agree,Strongly agree,Agree,Neutral,Agree,Neutral,Agree,Agree,Agree,Neutral,Agree,Disagree,Strongly agree,Agree,Neutral,Agree,Neutral,Strongly disagree,Yes,Weekend (Sat & Sun),45 minutes,Neutral,Strongly Agree,Strongly Agree,Disagree,Strongly Agree,Yes,Very Important,Very Important,Important,Very Important,Important,Very Important,Very Important,Very Important,Yes,Nil
+22,Female,Single,Employee,10001 to 25000,Graduate,3,12.9698,77.75,560066,Food delivery apps, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Agree,Strongly agree,Neutral,Strongly agree,Agree,Neutral,Neutral,Strongly agree,Agree,Disagree,Disagree,Disagree,Disagree,Agree,Neutral,Neutral,Neutral,Neutral,Strongly disagree,Yes,Weekend (Sat & Sun),45 minutes,Strongly Agree,Agree,Neutral,Disagree,Neutral,Yes,Very Important,Important,Moderately Important,Important,Very Important,Very Important,Important,Important,Yes,NIL
+24,Female,Single,Student,No Income,Ph.D,3,12.9438,77.5738,560004,Food delivery apps, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Strongly agree,Agree,Neutral,Agree,Agree,Strongly disagree,Agree,Strongly agree,Agree,Agree,Neutral,Disagree,Agree,Agree,Strongly agree,Agree,Neutral,Agree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Neutral,Agree,Strongly Agree,Agree,Yes,Important,Very Important,Important,Moderately Important,Important,Moderately Important,Important,Important,Yes,Need more delivery bandwidth even in rural areas
+26,Male,Single,Employee,25001 to 50000,Graduate,2,12.9261,77.6221,560034,Food delivery apps, Walk-in,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Ice cream / Cool drinks,Agree,Neutral,Agree,Disagree,Agree,Agree,Neutral,Strongly disagree,Neutral,Agree,Neutral,Disagree,Agree,Neutral,Neutral,Agree,Neutral,Disagree,Agree,Neutral,Yes,Anytime (Mon-Sun),15 minutes,Agree,Agree,Strongly Agree,Neutral,Agree,No,Important,Important,Moderately Important,Important,Important,Slightly Important,Important,Moderately Important,Yes,Requires professional packaging with no leakage
+28,Male,Married,Employee,More than 50000,Graduate,3,12.9698,77.75,560066,Food delivery apps, Web browser,Lunch, Dinner,Veg foods (Breakfast / Lunch / Dinner), Sweets,Neutral,Agree,Agree,Agree,Neutral,Agree,Strongly agree,Disagree,Agree,Agree,Neutral,Agree,Disagree,Agree,Agree,Neutral,Strongly agree,Agree,Neutral,Agree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Neutral,Strongly Agree,Strongly Agree,Neutral,Yes,Very Important,Very Important,Very Important,Very Important,Important,Important,Important,Important,No,NIL
+26,Male,Single,Employee,More than 50000,Post Graduate,2,12.9698,77.75,560066,Food delivery apps, Walk-in,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Strongly agree,Agree,Disagree,Agree,Strongly agree,Disagree,Strongly agree,Agree,Agree,Agree,Neutral,Agree,Agree,Neutral,Agree,Strongly agree,Agree,Neutral,Yes,Weekend (Sat & Sun),45 minutes,Strongly disagree,Agree,Strongly Agree,Agree,Agree,Yes,Important,Moderately Important,Important,Important,Important,Moderately Important,Very Important,Important,No,Need more delivery people to cover all orders
+25,Male,Single,Student,No Income,Post Graduate,1,12.9343,77.6044,560029,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Neutral,Agree,Neutral,Strongly agree,Agree,Strongly agree,Strongly agree,Strongly agree,Neutral,Strongly agree,Strongly agree,Strongly agree,Agree,Strongly agree,Neutral,Yes,Weekend (Sat & Sun),30 minutes,Strongly Agree,Neutral,Agree,Neutral,Neutral,Yes,Important,Moderately Important,Important,Moderately Important,Important,Important,Important,Important,Yes,Nil
+25,Male,Single,Employee,Below Rs.10000,Graduate,2,12.9698,77.75,560066,Food delivery apps, Web browser,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Neutral,Agree,Agree,Neutral,Agree,Agree,Neutral,Agree,Agree,Neutral,Agree,Agree,Agree,Neutral,Agree,Agree,Neutral,Agree,Yes,Anytime (Mon-Sun),30 minutes,Disagree,Neutral,Agree,Agree,Neutral,No,Important,Very Important,Slightly Important,Very Important,Slightly Important,Slightly Important,Important,Important,No,NIL
+18,Male,Single,Student,No Income,Graduate,5,12.9635,77.5821,560002,Food delivery apps, Web browser,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Ice cream / Cool drinks,Agree,Strongly agree,Agree,Neutral,Agree,Strongly agree,Strongly agree,Agree,Neutral,Agree,Disagree,Disagree,Disagree,Neutral,Strongly agree,Neutral,Agree,Strongly disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Agree,Neutral,Agree,Agree,Yes,Important,Moderately Important,Important,Important,Very Important,Important,Moderately Important,Important,Yes,NIL
+21,Male,Single,Student,No Income,Post Graduate,4,12.977,77.5773,560009,Food delivery apps, Walk-in,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Neutral,Agree,Neutral,Neutral,Neutral,Neutral,Neutral,Agree,Agree,Agree,Disagree,Neutral,Strongly disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Agree,Agree,Agree,Strongly Agree,Yes,Moderately Important,Important,Very Important,Very Important,Important,Moderately Important,Very Important,Very Important,Yes,nil 
+25,Male,Single,Student,No Income,Post Graduate,1,12.9343,77.6044,560029,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Neutral,Agree,Neutral,Strongly agree,Agree,Strongly agree,Strongly agree,Strongly agree,Neutral,Strongly agree,Strongly agree,Strongly agree,Agree,Strongly agree,Neutral,Yes,Weekend (Sat & Sun),30 minutes,Strongly Agree,Neutral,Agree,Neutral,Neutral,Yes,Important,Moderately Important,Important,Moderately Important,Important,Important,Important,Important,Yes,Nil
+25,Male,Single,Student,Below Rs.10000,Post Graduate,2,12.9925,77.5633,560021,Walk-in, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Disagree,Agree,Strongly agree,Neutral,Neutral,Agree,Neutral,Agree,Agree,Disagree,Neutral,Agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Disagree,Disagree,Disagree,Yes,Weekend (Sat & Sun),45 minutes,Strongly disagree,Strongly disagree,Strongly Agree,Strongly disagree,Strongly disagree,Yes,Important,Important,Important,Very Important,Very Important,Very Important,Very Important,Very Important,Yes,No
+23,Female,Single,Student,No Income,Graduate,5,13.0206,77.6479,560043,Food delivery apps, Direct call,Lunch, Snacks,Veg foods (Breakfast / Lunch / Dinner), Ice cream / Cool drinks,Strongly agree,Agree,Agree,Neutral,Agree,Neutral,Neutral,Agree,Neutral,Agree,Agree,Neutral,Agree,Disagree,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Yes,Weekdays (Mon-Fri),45 minutes,Neutral,Neutral,Neutral,Neutral,Neutral,Yes,Slightly Important,Moderately Important,Slightly Important,Slightly Important,Important,Important,Important,Important,Yes,Nil
+23,Male,Single,Employee,10001 to 25000,Post Graduate,2,12.985,77.5533,560010,Food delivery apps, Direct call,Lunch, Dinner,Veg foods (Breakfast / Lunch / Dinner), Sweets,Agree,Agree,Neutral,Agree,Agree,Neutral,Disagree,Agree,Neutral,Disagree,Neutral,Disagree,Agree,Agree,Agree,Agree,Disagree,Neutral,Agree,Agree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Neutral,Agree,Agree,Neutral,Yes,Very Important,Very Important,Very Important,Very Important,Moderately Important,Important,Very Important,Slightly Important,Yes,Want different cuisine dishes options in my location
+25,Female,Married,Employee,25001 to 50000,Graduate,4,12.9551,77.6593,560017,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Ice cream / Cool drinks,Disagree,Strongly disagree,Strongly disagree,Strongly disagree,Disagree,Neutral,Strongly disagree,Agree,Agree,Strongly agree,Strongly agree,Neutral,Agree,Strongly agree,Agree,Neutral,Agree,Agree,Strongly agree,Agree,Maybe,Anytime (Mon-Sun),15 minutes,Agree,Strongly Agree,Agree,Neutral,Agree,Yes,Very Important,Very Important,Very Important,Very Important,Important,Important,Moderately Important,Important,No,I had bad experience with delivery person. They need to be polite
+31,Female,Married,House wife,No Income,School,5,13.0289,77.54,560022,Food delivery apps, Web browser,Breakfast, Snacks,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Neutral,Agree,Strongly agree,Agree,Strongly agree,Agree,Agree,Agree,Disagree,Disagree,Disagree,Strongly disagree,Strongly disagree,Agree,Agree,Neutral,Agree,Neutral,Agree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Neutral,Agree,Disagree,Agree,Yes,Important,Very Important,Very Important,Very Important,Slightly Important,Moderately Important,Important,Very Important,Yes,I'm happy with the service of online delivery startups
+24,Male,Prefer not to say,Self Employeed,More than 50000,Ph.D,2,13.0138,77.5877,560006,Food delivery apps, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Disagree,Disagree,Strongly disagree,Disagree,Strongly disagree,Disagree,Agree,Agree,Strongly agree,Strongly agree,Strongly agree,Agree,Strongly agree,Agree,Strongly agree,Agree,Strongly agree,Strongly agree,Agree,Yes,Anytime (Mon-Sun),15 minutes,Disagree,Strongly disagree,Strongly disagree,Agree,Agree,Yes,Very Important,Very Important,Very Important,Very Important,Important,Moderately Important,Moderately Important,Slightly Important,No,I need the order to be delivered fast to my location
+32,Female,Married,Employee,25001 to 50000,Graduate,5,12.9261,77.6221,560034,Food delivery apps, Web browser,Breakfast, Lunch,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Agree,Strongly agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Disagree,Neutral,Neutral,Disagree,Disagree,Strongly disagree,Disagree,Agree,Agree,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),60 minutes,Agree,Agree,Agree,Agree,Disagree,Yes,Important,Moderately Important,Important,Moderately Important,Moderately Important,Moderately Important,Very Important,Very Important,Yes,I'm satisfied with the service of swiggy
+25,Male,Single,Employee,25001 to 50000,Graduate,3,12.9766,77.5993,560001,Food delivery apps, Direct call,Breakfast, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Strongly agree,Neutral,Agree,Strongly agree,Disagree,Disagree,Agree,Disagree,Neutral,Disagree,Disagree,Disagree,Agree,Agree,Disagree,Disagree,Strongly disagree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Strongly Agree,Strongly Agree,Neutral,Agree,Yes,Very Important,Very Important,Important,Moderately Important,Slightly Important,Moderately Important,Important,Very Important,Yes,"I'm satisfied with zomato, I'm also a premium member"
+27,Female,Married,Self Employeed,More than 50000,Graduate,5,12.9766,77.5993,560001,Food delivery apps, Walk-in,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Disagree,Agree,Agree,Disagree,Agree,Neutral,Agree,Strongly agree,Agree,Strongly agree,Neutral,Agree,Disagree,Agree,Agree,Agree,Disagree,Disagree,Strongly disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Strongly Agree,Agree,Strongly Agree,Yes,Very Important,Important,Very Important,Very Important,Important,Important,Important,Important,No,"Freshness is important but when it is packed by the usual package, I feel it's losing it is freshness and quality"
+26,Male,Single,Self Employeed,25001 to 50000,Graduate,3,12.9766,77.5993,560001,Food delivery apps, Web browser,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Disagree,Disagree,Agree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Agree,Disagree,Agree,Disagree,Disagree,Disagree,No,Weekdays (Mon-Fri),More than 60 minutes,Agree,Agree,Strongly Agree,Agree,Strongly Agree,No,Important,Important,Slightly Important,Slightly Important,Important,Important,Important,Important,Yes,I have no problem with the delivery service. I'm satisfied
+26,Female,Single,Self Employeed,25001 to 50000,Post Graduate,3,12.9635,77.5821,560002,Food delivery apps, Web browser,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Agree,Agree,Strongly agree,Strongly agree,Disagree,Disagree,Disagree,Disagree,Disagree,Strongly disagree,Strongly disagree,Disagree,Disagree,Strongly disagree,Strongly disagree,Neutral,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Agree,Neutral,Agree,Agree,Yes,Very Important,Very Important,Very Important,Very Important,Important,Important,Important,Important,Yes,I love ordering from online as they have more varieties and discounts
+32,Male,Married,Employee,More than 50000,Ph.D,5,12.9635,77.5821,560002,Food delivery apps, Web browser,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Agree,Strongly agree,Agree,Neutral,Agree,Strongly agree,Agree,Strongly agree,Disagree,Disagree,Disagree,Disagree,Disagree,Strongly disagree,Disagree,Strongly disagree,Disagree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Strongly Agree,Agree,Agree,Agree,Yes,Very Important,Important,Moderately Important,Important,Important,Moderately Important,Very Important,Very Important,Yes,Online delivery requires much hygiene consciousness now than ever before   
+24,Male,Married,Self Employeed,More than 50000,Ph.D,6,12.9635,77.5821,560002,Food delivery apps, Direct call,Dinner, Dinner,Veg foods (Breakfast / Lunch / Dinner), Sweets,Disagree,Disagree,Disagree,Disagree,Strongly disagree,Disagree,Strongly disagree,Agree,Strongly agree,Strongly agree,Strongly agree,Agree,Strongly agree,Strongly agree,Agree,Strongly agree,Strongly agree,Strongly agree,Agree,Strongly agree,Yes,Anytime (Mon-Sun),30 minutes,Disagree,Agree,Strongly Agree,Strongly Agree,Strongly Agree,Yes,Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,No,"I had bad experiences with delivery person, they need to be polite when asking for location and avoid asking us to come to a particular location to get the food"
+27,Female,Married,Self Employeed,25001 to 50000,Graduate,3,12.9635,77.5821,560002,Food delivery apps, Web browser,Breakfast, Lunch,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Strongly agree,Strongly agree,Agree,Strongly agree,Strongly agree,Disagree,Disagree,Agree,Disagree,Disagree,Strongly disagree,Strongly disagree,Agree,Strongly agree,Agree,Neutral,Strongly disagree,Strongly disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Agree,Strongly Agree,Agree,Yes,Important,Very Important,Very Important,Very Important,Slightly Important,Moderately Important,Important,Very Important,Yes,I'm satisfied with the delivery service
+23,Male,Single,Student,No Income,Post Graduate,3,12.977,77.5773,560009,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Agree,Neutral,Neutral,Agree,Agree,Agree,Agree,Neutral,Neutral,Agree,Neutral,Neutral,Agree,Agree,Agree,Strongly agree,Neutral,Yes,Weekend (Sat & Sun),30 minutes,Disagree,Agree,Agree,Agree,Agree,No,Important,Important,Moderately Important,Very Important,Very Important,Important,Important,Important,Yes,NIL
+25,Male,Single,Student,No Income,Post Graduate,4,12.977,77.5773,560009,Food delivery apps, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Ice cream / Cool drinks,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Disagree,Disagree,Agree,Disagree,Disagree,Agree,Agree,Agree,Strongly agree,Agree,Agree,Agree,Strongly agree,Yes,Anytime (Mon-Sun),30 minutes,Strongly Agree,Agree,Agree,Disagree,Strongly Agree,Yes,Very Important,Very Important,Important,Very Important,Very Important,Important,Very Important,Very Important,Yes,None
+23,Male,Single,Student,No Income,Post Graduate,3,12.977,77.5773,560009,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Agree,Neutral,Neutral,Agree,Agree,Agree,Agree,Neutral,Neutral,Agree,Neutral,Neutral,Agree,Agree,Agree,Strongly agree,Neutral,Yes,Weekend (Sat & Sun),30 minutes,Disagree,Agree,Agree,Agree,Agree,No,Important,Important,Moderately Important,Very Important,Very Important,Important,Important,Important,Yes,NIL
+23,Female,Single,Student,No Income,Post Graduate,4,13.0487,77.5923,560024,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Strongly agree,Strongly agree,Neutral,Neutral,Strongly agree,Strongly agree,Neutral,Disagree,Neutral,Strongly agree,Agree,Neutral,Disagree,Agree,Agree,Agree,Strongly agree,Agree,Neutral,Yes,Weekend (Sat & Sun),45 minutes,Neutral,Neutral,Neutral,Neutral,Neutral,Yes,Important,Important,Moderately Important,Moderately Important,Very Important,Very Important,Very Important,Very Important,Yes,Nil
+28,Male,Married,Employee,More than 50000,Post Graduate,3,13.0019,77.5713,560003,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Strongly agree,Strongly agree,Agree,Strongly agree,Strongly disagree,Disagree,Agree,Disagree,Strongly disagree,Strongly disagree,Strongly disagree,Disagree,Strongly disagree,Agree,Disagree,Disagree,Disagree,No,Anytime (Mon-Sun),60 minutes,Agree,Strongly Agree,Strongly Agree,Agree,Agree,No,Important,Important,Slightly Important,Important,Important,Very Important,Important,Very Important,Yes,I'm satisfied with the current service of swiggy and zomato
+32,Female,Married,Employee,More than 50000,Graduate,1,13.0019,77.5713,560003,Food delivery apps, Web browser,Breakfast, Lunch,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Neutral,Agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Disagree,Disagree,Agree,Agree,Strongly agree,Agree,Agree,Disagree,Agree,Agree,Disagree,Agree,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Strongly Agree,Disagree,Disagree,Yes,Very Important,Important,Important,Very Important,Important,Moderately Important,Very Important,Very Important,No,I require faster delivery with minimum calls
+23,Male,Single,Student,No Income,Post Graduate,2,13.0019,77.5713,560003,Food delivery apps, Direct call,Breakfast, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Strongly agree,Strongly agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Agree,Disagree,Disagree,Disagree,Agree,Strongly agree,Strongly agree,Disagree,Strongly agree,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Agree,Strongly Agree,Disagree,Disagree,Yes,Important,Very Important,Very Important,Very Important,Important,Important,Important,Important,Yes,NIL
+19,Male,Single,Student,No Income,Graduate,2,13.0019,77.5713,560003,Food delivery apps, Web browser,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Disagree,Agree,Neutral,Disagree,Disagree,Agree,Disagree,Strongly disagree,Agree,Disagree,Agree,Disagree,Disagree,Agree,Agree,Agree,Agree,Strongly agree,Strongly agree,Yes,Anytime (Mon-Sun),15 minutes,Agree,Strongly Agree,Agree,Strongly Agree,Agree,No,Important,Very Important,Very Important,Important,Important,Very Important,Important,Important,No,I had bad experiences with the food delivered to me 
+19,Female,Single,Student,No Income,Graduate,4,12.9537,77.6176,560047,Food delivery apps, Walk-in,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Sweets,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Neutral,Agree,Neutral,Agree,Agree,Neutral,Agree,Agree,Neutral,Strongly agree,Strongly agree,Strongly agree,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),45 minutes,Disagree,Agree,Agree,Disagree,Agree,Yes,Important,Important,Slightly Important,Slightly Important,Very Important,Important,Important,Important,Yes,nil
+27,Female,Married,Employee,25001 to 50000,Post Graduate,2,12.9698,77.75,560066,Food delivery apps, Walk-in,Lunch, Snacks,Veg foods (Breakfast / Lunch / Dinner), Ice cream / Cool drinks,Agree,Neutral,Disagree,Neutral,Neutral,Neutral,Neutral,Neutral,Strongly agree,Agree,Neutral,Agree,Strongly agree,Strongly agree,Strongly agree,Agree,Agree,Strongly agree,Strongly agree,Agree,Maybe,Anytime (Mon-Sun),30 minutes,Neutral,Disagree,Disagree,Neutral,Neutral,Yes,Important,Very Important,Slightly Important,Moderately Important,Very Important,Very Important,Very Important,Very Important,No,Nil 
+25,Male,Single,Self Employeed,25001 to 50000,Graduate,3,12.998,77.6227,560005,Food delivery apps, Web browser,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Strongly agree,Agree,Strongly agree,Agree,Disagree,Strongly disagree,Disagree,Strongly disagree,Disagree,Strongly disagree,Disagree,Agree,Strongly disagree,Agree,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Neutral,Agree,Neutral,Agree,Yes,Important,Very Important,Important,Very Important,Important,Important,Important,Important,Yes,I love swiggy !!
+33,Male,Married,Employee,More than 50000,Ph.D,5,12.998,77.6227,560005,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Disagree,Disagree,Disagree,Disagree,Strongly disagree,Disagree,Agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Disagree,Agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Neutral,Agree,Agree,Agree,Yes,Very Important,Very Important,Very Important,Very Important,Very Important,Moderately Important,Important,Very Important,No,I once had damaged package from zomato and they declined to offer me refund.worst behavior !
+26,Female,Single,Employee,More than 50000,Graduate,3,12.998,77.6227,560005,Food delivery apps, Web browser,Breakfast, Lunch,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Strongly agree,Strongly agree,Agree,Strongly agree,Disagree,Disagree,Agree,Disagree,Neutral,Disagree,Disagree,Agree,Strongly agree,Agree,Agree,Neutral,Agree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Strongly Agree,Agree,Agree,Agree,No,Slightly Important,Slightly Important,Slightly Important,Slightly Important,Slightly Important,Slightly Important,Slightly Important,Slightly Important,Yes,I'm satisfied with swiggy and their wonderful offers
+22,Female,Single,Student,Below Rs.10000,Post Graduate,4,12.9343,77.6044,560029,Food delivery apps, Direct call,Lunch, Snacks,Non Veg foods (Lunch / Dinner), Sweets,Strongly agree,Neutral,Agree,Agree,Agree,Neutral,Agree,Agree,Neutral,Agree,Agree,Agree,Strongly agree,Agree,Strongly agree,Agree,Agree,Agree,Agree,Agree,Yes,Weekend (Sat & Sun),45 minutes,Disagree,Neutral,Disagree,Disagree,Disagree,Yes,Important,Moderately Important,Important,Moderately Important,Very Important,Important,Important,Important,Yes,Nil
+23,Male,Single,Student,No Income,Post Graduate,3,13.102,77.5864,560064,Food delivery apps, Walk-in,Lunch, Dinner,Veg foods (Breakfast / Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Strongly agree,Agree,Agree,Agree,Strongly agree,Agree,Strongly agree,Strongly agree,Strongly agree,Disagree,Strongly agree,Neutral,Yes,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Strongly Agree,Agree,Agree,Yes,Important,Important,Important,Important,Very Important,Very Important,Very Important,Very Important,No,Nil
+22,Female,Single,Student,No Income,Graduate,3,13.0158,77.539,560096,Food delivery apps, Walk-in,Snacks, Dinner,Veg foods (Breakfast / Lunch / Dinner), Sweets,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Agree,Strongly agree,Strongly agree,Agree,Disagree,Neutral,Disagree,Strongly disagree,Disagree,Agree,Disagree,Disagree,Agree,Agree,Strongly agree,Yes,Anytime (Mon-Sun),45 minutes,Neutral,Agree,Agree,Agree,Agree,Yes,Important,Important,Slightly Important,Slightly Important,Very Important,Very Important,Very Important,Very Important,Yes,"Prices of food items differ while using different smartphones to order at the same time. So, there is no uniform pricing system in food delivery apps. But yes, they add a lot of convenience in our lives!"
+25,Male,Married,Employee,25001 to 50000,Graduate,2,12.998,77.6227,560005,Food delivery apps, Web browser,Breakfast, Snacks,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Disagree,Disagree,Disagree,Neutral,Disagree,Agree,Agree,Strongly agree,Agree,Strongly agree,Agree,Strongly agree,Disagree,Agree,Agree,Agree,Strongly agree,Strongly agree,Strongly agree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Strongly Agree,Strongly Agree,Strongly Agree,Agree,Maybe,Important,Important,Very Important,Very Important,Moderately Important,Moderately Important,Important,Moderately Important,Yes,I'm okay ordering online for food
+30,Female,Married,House wife,No Income,School,5,12.998,77.6227,560005,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Agree,Disagree,Disagree,Disagree,Disagree,Strongly disagree,Disagree,Agree,Agree,Disagree,Disagree,Strongly disagree,Disagree,No,Weekdays (Mon-Fri),30 minutes,Agree,Agree,Strongly Agree,Agree,Strongly Agree,No,Important,Very Important,Very Important,Very Important,Important,Very Important,Very Important,Very Important,Yes,I love ordering candies for my children from swiggy
+24,Male,Single,Student,No Income,Post Graduate,3,13.0138,77.5877,560006,Food delivery apps, Web browser,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Disagree,Strongly agree,Strongly disagree,Disagree,Disagree,Agree,Agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Disagree,Neutral,Agree,Strongly agree,Strongly agree,Neutral,Yes,Anytime (Mon-Sun),15 minutes,Agree,Strongly Agree,Agree,Strongly Agree,Agree,Yes,Important,Very Important,Important,Very Important,Very Important,Important,Very Important,Very Important,No,I hate zomato and food panda. Poor tracking and poor customer service
+25,Male,Single,Employee,10001 to 25000,Post Graduate,3,13.0138,77.5877,560006,Food delivery apps, Web browser,Breakfast, Snacks,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Strongly agree,Strongly agree,Agree,Strongly agree,Disagree,Disagree,Agree,Disagree,Disagree,Disagree,Strongly disagree,Agree,Strongly agree,Strongly agree,Disagree,Disagree,Strongly disagree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Strongly Agree,Strongly Agree,Agree,Agree,No,Important,Important,Very Important,Very Important,Slightly Important,Moderately Important,Moderately Important,Slightly Important,Yes,Best service is provided by swiggy
+23,Male,Single,Student,More than 50000,Post Graduate,3,12.977,77.5773,560009,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Strongly agree,Agree,Strongly agree,Strongly agree,Agree,Strongly agree,Agree,Neutral,Disagree,Neutral,Neutral,Agree,Strongly disagree,Neutral,Agree,Agree,Strongly agree,Strongly agree,Agree,Yes,Anytime (Mon-Sun),45 minutes,Strongly Agree,Strongly Agree,Strongly Agree,Strongly disagree,Strongly Agree,Yes,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Yes,Nil 
+24,Female,Single,Student,No Income,Post Graduate,4,13.0496,77.4941,560073,Food delivery apps, Direct call,Lunch, Dinner,Veg foods (Breakfast / Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Agree,Neutral,Agree,Neutral,Neutral,Strongly agree,Neutral,Strongly agree,Agree,Neutral,Neutral,Agree,Agree,Agree,Disagree,Neutral,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Neutral,Neutral,Strongly disagree,Neutral,No,Very Important,Very Important,Very Important,Important,Very Important,Very Important,Very Important,Very Important,Yes,Nil
+32,Male,Married,Employee,10001 to 25000,Graduate,4,12.9783,77.6408,560038,Food delivery apps, Walk-in,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Neutral,Neutral,Agree,Disagree,Neutral,Agree,Agree,Agree,Neutral,Neutral,Agree,Neutral,Agree,Neutral,Agree,Disagree,Disagree,Disagree,Yes,Weekend (Sat & Sun),30 minutes,Neutral,Neutral,Agree,Neutral,Agree,Yes,Moderately Important,Important,Important,Moderately Important,Moderately Important,Slightly Important,Moderately Important,Important,Yes,NIL
+22,Male,Single,Student,No Income,Post Graduate,4,12.9889,77.5741,560020,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Neutral,Neutral,Neutral,Neutral,Agree,Disagree,Agree,Disagree,Neutral,Agree,Disagree,Neutral,Disagree,Neutral,Agree,Disagree,Neutral,Neutral,Disagree,Agree,Yes,Anytime (Mon-Sun),30 minutes,Disagree,Neutral,Disagree,Agree,Neutral,Yes,Slightly Important,Moderately Important,Important,Slightly Important,Moderately Important,Important,Very Important,Important,Yes,NIL
+23,Male,Single,Student,More than 50000,Post Graduate,3,12.977,77.5773,560009,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Strongly agree,Agree,Strongly agree,Strongly agree,Agree,Strongly agree,Agree,Neutral,Disagree,Neutral,Neutral,Agree,Strongly disagree,Neutral,Agree,Agree,Strongly agree,Strongly agree,Agree,Yes,Anytime (Mon-Sun),45 minutes,Strongly Agree,Strongly Agree,Strongly Agree,Strongly disagree,Strongly Agree,Yes,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Yes,Nil 
+23,Female,Single,Student,No Income,Post Graduate,4,13.0487,77.5923,560024,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Strongly agree,Strongly agree,Neutral,Neutral,Strongly agree,Strongly agree,Neutral,Disagree,Neutral,Strongly agree,Agree,Neutral,Disagree,Agree,Agree,Agree,Strongly agree,Agree,Neutral,Yes,Weekend (Sat & Sun),45 minutes,Neutral,Neutral,Neutral,Neutral,Neutral,Yes,Important,Important,Moderately Important,Moderately Important,Very Important,Very Important,Very Important,Very Important,Yes,Nil
+20,Male,Single,Student,No Income,Graduate,2,12.9579,77.6309,560007,Food delivery apps, Direct call,Breakfast, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Neutral,Disagree,Agree,Disagree,Disagree,Disagree,Strongly disagree,Agree,Disagree,Agree,Disagree,Disagree,Strongly disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Agree,Agree,Strongly Agree,Yes,Important,Very Important,Important,Very Important,Important,Important,Important,Important,Yes,Swiggy and zomato are the best food delivery apps
+21,Male,Single,Student,No Income,Graduate,2,12.9579,77.6309,560007,Food delivery apps, Web browser,Breakfast, Snacks,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Strongly agree,Agree,Strongly agree,Strongly agree,Strongly agree,Disagree,Strongly disagree,Disagree,Strongly disagree,Disagree,Disagree,Agree,Agree,Agree,Strongly agree,Strongly agree,Agree,Strongly agree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Agree,Strongly Agree,Agree,Yes,Important,Very Important,Important,Very Important,Moderately Important,Slightly Important,Moderately Important,Important,Yes,I have no issues with the delivery 
+24,Female,Married,Self Employeed,10001 to 25000,Graduate,5,12.9579,77.6309,560007,Food delivery apps, Web browser,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Disagree,Agree,Neutral,Disagree,Agree,Agree,Agree,Agree,Strongly agree,Agree,Strongly agree,Agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Strongly disagree,Strongly disagree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Strongly Agree,Agree,Agree,Agree,Yes,Important,Important,Very Important,Important,Important,Moderately Important,Moderately Important,Important,Yes,Food delivery apps are performing well in Bangalore. But poor delivery when it comes to rural places
+23,Female,Single,Student,No Income,Post Graduate,4,13.0487,77.5923,560024,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Strongly agree,Strongly agree,Neutral,Neutral,Strongly agree,Strongly agree,Neutral,Disagree,Neutral,Strongly agree,Agree,Neutral,Disagree,Agree,Agree,Agree,Strongly agree,Agree,Neutral,Yes,Weekend (Sat & Sun),45 minutes,Neutral,Neutral,Neutral,Neutral,Neutral,Yes,Important,Important,Moderately Important,Moderately Important,Very Important,Very Important,Very Important,Very Important,Yes,Nil
+25,Male,Single,Employee,25001 to 50000,Post Graduate,3,12.985,77.5533,560010,Food delivery apps, Web browser,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Strongly agree,Strongly agree,Disagree,Disagree,Disagree,Disagree,Agree,Disagree,Disagree,Strongly disagree,Strongly disagree,Agree,Strongly agree,Agree,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Agree,Neutral,Neutral,Yes,Important,Important,Very Important,Important,Important,Important,Very Important,Slightly Important,Yes,I love being a part of zomato premium member. I get amazing offers
+32,Female,Married,House wife,No Income,Graduate,3,12.985,77.5533,560010,Direct call, Web browser,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Strongly agree,Strongly agree,Agree,Neutral,Neutral,Agree,Agree,Disagree,Agree,Disagree,Disagree,Strongly disagree,Strongly agree,Strongly agree,Strongly agree,Agree,Strongly agree,Strongly disagree,Yes,Anytime (Mon-Sun),60 minutes,Agree,Strongly Agree,Agree,Strongly Agree,Agree,Yes,Important,Very Important,Important,Very Important,Moderately Important,Slightly Important,Important,Important,Yes,"Whenever I'm not well, use dunzo to order for my whole family "
+27,Male,Married,Employee,More than 50000,Ph.D,5,12.985,77.5533,560010,Food delivery apps, Web browser,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Ice cream / Cool drinks,Agree,Disagree,Neutral,Disagree,Neutral,Disagree,Strongly disagree,Agree,Agree,Strongly agree,Strongly agree,Agree,Disagree,Disagree,Agree,Strongly agree,Agree,Agree,Strongly agree,Strongly disagree,Yes,Anytime (Mon-Sun),15 minutes,Agree,Strongly Agree,Agree,Strongly Agree,Strongly Agree,Yes,Important,Very Important,Important,Very Important,Very Important,Very Important,Very Important,Very Important,No,Worst behavior by customer care. They process refund quite late
+20,Female,Single,Student,No Income,Graduate,2,12.9337,77.59,560011,Food delivery apps, Web browser,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Ice cream / Cool drinks,Strongly agree,Agree,Strongly agree,Agree,Strongly agree,Strongly agree,Strongly agree,Disagree,Agree,Disagree,Disagree,Disagree,Strongly disagree,Disagree,Neutral,Agree,Disagree,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Agree,Strongly Agree,Strongly Agree,Strongly Agree,Yes,Important,Very Important,Important,Moderately Important,Important,Important,Moderately Important,Important,Yes,Best delivery people working hard for their family. respect them
+21,Male,Single,Student,No Income,Graduate,2,12.9337,77.59,560011,Food delivery apps, Walk-in,Breakfast, Lunch,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Strongly agree,Agree,Strongly agree,Strongly agree,Strongly agree,Disagree,Strongly disagree,Neutral,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Disagree,Disagree,Agree,Agree,Agree,Strongly disagree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Strongly Agree,Strongly Agree,Agree,Strongly Agree,Yes,Important,Very Important,Very Important,Very Important,Important,Important,Important,Important,Yes,I have no issues with the food delivery service. They're good
+26,Male,Single,Employee,More than 50000,Post Graduate,3,12.9337,77.59,560011,Food delivery apps, Walk-in,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Strongly disagree,Disagree,Strongly disagree,Disagree,Strongly disagree,Disagree,Agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Disagree,Disagree,Neutral,Agree,Neutral,Disagree,Yes,Anytime (Mon-Sun),15 minutes,Agree,Strongly Agree,Strongly Agree,Strongly Agree,Strongly Agree,Yes,Important,Very Important,Important,Very Important,Important,Important,Important,Important,No,I can't blame the delivery people for late delivery. It lies in the logistics department of such startups.
+25,Male,Single,Employee,10001 to 25000,Graduate,4,13.0166,77.6804,560016,Food delivery apps, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Agree,Strongly agree,Strongly agree,Strongly agree,Agree,Strongly agree,Agree,Neutral,Strongly agree,Disagree,Neutral,Agree,Strongly agree,Disagree,Disagree,Disagree,Disagree,Disagree,Agree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Agree,Agree,Strongly disagree,Agree,Yes,Very Important,Very Important,Unimportant,Very Important,Very Important,Important,Very Important,Very Important,Yes,NIL
+26,Male,Single,Employee,25001 to 50000,Post Graduate,3,13.014,77.5658,560012,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Strongly agree,Strongly agree,Strongly agree,Agree,Disagree,Strongly disagree,Neutral,Disagree,Strongly disagree,Disagree,Strongly disagree,Disagree,Neutral,Agree,Neutral,Disagree,Disagree,No,Anytime (Mon-Sun),More than 60 minutes,Agree,Strongly Agree,Agree,Agree,Agree,No,Important,Important,Moderately Important,Important,Moderately Important,Moderately Important,Moderately Important,Moderately Important,Yes,I'm satisfied with the service and swiggy is the best
+26,Female,Married,Employee,25001 to 50000,Graduate,3,13.014,77.5658,560012,Food delivery apps, Direct call,Breakfast, Lunch,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Strongly agree,Agree,Agree,Agree,Agree,Agree,Disagree,Disagree,Disagree,Strongly disagree,Disagree,Agree,Strongly agree,Agree,Agree,Disagree,Strongly disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Agree,Strongly Agree,Agree,Yes,Important,Important,Important,Important,Important,Very Important,Important,Important,Yes,I love food delivery service. They are amazing 
+27,Male,Single,Employee,More than 50000,Ph.D,4,13.0503,77.5529,560013,Food delivery apps, Walk-in,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Agree,Agree,Agree,Agree,Neutral,Agree,Disagree,Agree,Neutral,Disagree,Strongly disagree,Agree,Agree,Agree,Strongly disagree,Strongly disagree,Strongly disagree,Yes,Anytime (Mon-Sun),15 minutes,Agree,Strongly Agree,Agree,Agree,Strongly Agree,Maybe,Important,Very Important,Important,Very Important,Important,Important,Important,Very Important,No,"I require faster delivery. Nowadays due to covid, the delivery service is slow"
+27,Female,Single,Student,No Income,Ph.D,5,13.0503,77.5529,560013,Food delivery apps, Walk-in,Breakfast, Lunch,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Disagree,Strongly disagree,Disagree,Strongly disagree,Disagree,Disagree,Agree,Neutral,Agree,Agree,Agree,Disagree,Disagree,Agree,Agree,Agree,Disagree,Disagree,Strongly disagree,Yes,Anytime (Mon-Sun),15 minutes,Disagree,Strongly Agree,Agree,Agree,Neutral,Yes,Important,Important,Important,Important,Slightly Important,Moderately Important,Important,Important,No,A delivery person abused me for marking the location wrong. I can understand his tedious work. But abusing with such vulgar words is not tolerable
+24,Female,Single,Student,No Income,Post Graduate,5,12.9883,77.5987,560051,Food delivery apps, Walk-in,Breakfast, Dinner,Veg foods (Breakfast / Lunch / Dinner), Sweets,Agree,Strongly agree,Agree,Neutral,Disagree,Agree,Strongly agree,Agree,Agree,Disagree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Disagree,Disagree,Disagree,Disagree,Agree,Strongly agree,Maybe,Anytime (Mon-Sun),45 minutes,Strongly Agree,Neutral,Disagree,Neutral,Agree,Yes,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Yes,Nil
+25,Male,Married,Self Employeed,More than 50000,School,2,13.0626,77.5284,560015,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Strongly agree,Agree,Strongly agree,Agree,Strongly agree,Disagree,Disagree,Neutral,Disagree,Disagree,Strongly disagree,Strongly disagree,Neutral,Agree,Neutral,Neutral,Disagree,Disagree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Neutral,Disagree,Disagree,Disagree,Yes,Important,Very Important,Important,Very Important,Slightly Important,Slightly Important,Slightly Important,Slightly Important,Yes,Certainly a wonderful delivery service
+20,Male,Single,Student,No Income,Graduate,2,13.0626,77.5284,560015,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Disagree,Strongly disagree,Disagree,Disagree,Disagree,Disagree,Agree,Agree,Agree,Neutral,Agree,Neutral,Agree,Disagree,Disagree,Neutral,Disagree,Strongly disagree,Strongly disagree,Yes,Anytime (Mon-Sun),15 minutes,Agree,Strongly Agree,Agree,Strongly Agree,Agree,Yes,Important,Very Important,Important,Important,Moderately Important,Important,Important,Important,No,There are not much restaurant choices
+22,Male,Single,Student,No Income,Post Graduate,1,13.0626,77.5284,560015,Food delivery apps, Walk-in,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Strongly agree,Agree,Strongly agree,Strongly agree,Disagree,Disagree,Strongly disagree,Disagree,Disagree,Strongly disagree,Disagree,Agree,Agree,Agree,Agree,Agree,Agree,No,Anytime (Mon-Sun),More than 60 minutes,Agree,Strongly Agree,Agree,Strongly Agree,Agree,Maybe,Slightly Important,Slightly Important,Moderately Important,Slightly Important,Unimportant,Slightly Important,Unimportant,Slightly Important,Yes,No problem with food delivery startups
+26,Female,Married,Student,Below Rs.10000,Ph.D,3,13.0166,77.6804,560016,Food delivery apps, Web browser,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Sweets,Agree,Agree,Agree,Agree,Neutral,Neutral,Agree,Disagree,Disagree,Neutral,Disagree,Disagree,Strongly disagree,Strongly disagree,Agree,Strongly agree,Agree,Strongly disagree,Strongly disagree,Strongly disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Agree,Agree,Agree,Yes,Important,Very Important,Important,Important,Moderately Important,Important,Moderately Important,Important,Yes,NIL
+27,Male,Prefer not to say,Employee,25001 to 50000,Post Graduate,1,13.0166,77.6804,560016,Food delivery apps, Walk-in,Breakfast, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Agree,Agree,Agree,Agree,Disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Agree,Agree,Agree,Disagree,Disagree,Disagree,No,Weekend (Sat & Sun),45 minutes,Agree,Strongly Agree,Agree,Agree,Strongly Agree,Yes,Important,Very Important,Important,Important,Important,Very Important,Important,Important,Yes,NIL
+25,Female,Single,Student,No Income,Post Graduate,3,12.9551,77.6593,560017,Food delivery apps, Web browser,Breakfast, Snacks,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Agree,Strongly agree,Agree,Strongly agree,Disagree,Strongly disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Agree,Agree,Agree,Neutral,Agree,Agree,Yes,Anytime (Mon-Sun),30 minutes,Disagree,Disagree,Neutral,Neutral,Neutral,Maybe,Important,Moderately Important,Moderately Important,Important,Moderately Important,Moderately Important,Moderately Important,Moderately Important,Yes,"Best service, Crazy drivers !"
+24,Male,Single,Employee,Below Rs.10000,Graduate,2,12.9551,77.6593,560017,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Disagree,Disagree,Disagree,Disagree,Strongly disagree,Strongly disagree,Disagree,Neutral,Neutral,Neutral,Strongly disagree,Strongly disagree,Strongly disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Agree,Agree,Strongly Agree,Yes,Important,Very Important,Important,Important,Important,Important,Important,Important,Yes,I have no problem with delivery service. BEST service !
+23,Female,Single,Employee,10001 to 25000,Graduate,6,12.9551,77.6593,560017,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Agree,Agree,Strongly agree,Agree,Disagree,Disagree,Strongly disagree,Disagree,Disagree,Disagree,Strongly disagree,Agree,Strongly agree,Agree,Strongly disagree,Disagree,Disagree,Yes,Weekend (Sat & Sun),45 minutes,Neutral,Disagree,Disagree,Strongly disagree,Agree,Yes,Important,Very Important,Important,Moderately Important,Important,Important,Very Important,Important,Yes,I have good experience with swiggy. No issues
+22,Male,Single,Student,No Income,Graduate,2,12.957,77.5637,560018,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Strongly disagree,Disagree,Strongly disagree,Disagree,Disagree,Disagree,Agree,Agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Disagree,Neutral,Disagree,Agree,Agree,Agree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Strongly Agree,Neutral,Neutral,Agree,Yes,Important,Important,Important,Important,Important,Very Important,Very Important,Important,No,They really need to delivery fast
+26,Male,Married,Self Employeed,More than 50000,Post Graduate,3,12.957,77.5637,560018,Food delivery apps, Walk-in,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly disagree,Strongly disagree,Disagree,Strongly disagree,Disagree,Strongly disagree,Strongly disagree,Agree,Strongly agree,Agree,Agree,Disagree,Disagree,Strongly agree,Agree,Strongly agree,Agree,Agree,Strongly agree,Strongly agree,Yes,Weekdays (Mon-Fri),60 minutes,Agree,Agree,Strongly Agree,Agree,Strongly Agree,No,Important,Slightly Important,Slightly Important,Slightly Important,Slightly Important,Moderately Important,Important,Very Important,No,Not happy with the food quality when it is delivered
+26,Male,Single,Employee,Below Rs.10000,Post Graduate,1,12.957,77.5637,560018,Food delivery apps, Web browser,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Agree,Agree,Agree,Neutral,Disagree,Disagree,Disagree,Strongly disagree,Neutral,Disagree,Disagree,Agree,Agree,Neutral,Agree,Agree,Agree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Agree,Agree,Neutral,Neutral,Yes,Important,Very Important,Important,Very Important,Slightly Important,Moderately Important,Moderately Important,Important,Yes,"Tracking system needs to be improved, sometimes the delivery guy is still and doesn't show his movements"
+25,Female,Married,Self Employeed,25001 to 50000,Post Graduate,3,12.957,77.5637,560018,Food delivery apps, Web browser,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Neutral,Agree,Agree,Disagree,Agree,Agree,Disagree,Disagree,Neutral,Agree,Agree,Disagree,Disagree,Disagree,Neutral,Agree,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Agree,Neutral,Agree,Agree,Yes,Important,Important,Very Important,Important,Moderately Important,Important,Important,Moderately Important,No,"If delivery gets slow, I tend to give poor ratings on delivery "
+29,Female,Married,Employee,More than 50000,Graduate,3,12.957,77.5637,560018,Food delivery apps, Walk-in,Breakfast, Lunch,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Neutral,Disagree,Neutral,Neutral,Disagree,Disagree,Neutral,Agree,Agree,Agree,Neutral,Agree,Agree,Agree,Neutral,Agree,Neutral,Strongly disagree,Agree,Agree,Yes,Weekend (Sat & Sun),45 minutes,Agree,Strongly Agree,Agree,Neutral,Agree,Yes,Important,Moderately Important,Moderately Important,Moderately Important,Slightly Important,Moderately Important,Important,Moderately Important,No,I want both delivery to be quick as well as food quality to be good
+23,Male,Single,Student,Below Rs.10000,Graduate,3,12.8652,77.524,560109,Direct call, Walk-in,Dinner, Dinner,Veg foods (Breakfast / Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly disagree,Neutral,Disagree,Agree,Disagree,Neutral,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Agree,Neutral,Disagree,Agree,Maybe,Important,Moderately Important,Important,Moderately Important,Very Important,Very Important,Important,Important,Yes,Paid but never received it. No fresh and over paid compared to buying it directly 
+22,Female,Single,Employee,25001 to 50000,Graduate,4,12.9698,77.75,560066,Food delivery apps, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Neutral,Disagree,Agree,Disagree,Neutral,Agree,Strongly agree,Neutral,Agree,Neutral,Agree,Strongly disagree,Agree,Neutral,Neutral,Strongly agree,Strongly agree,Strongly agree,Maybe,Weekend (Sat & Sun),45 minutes,Strongly Agree,Agree,Agree,Strongly disagree,Strongly disagree,Yes,Slightly Important,Very Important,Unimportant,Important,Very Important,Important,Very Important,Moderately Important,Yes,NIL
+22,Male,Single,Student,No Income,Graduate,2,12.9889,77.5741,560020,Food delivery apps, Walk-in,Breakfast, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Neutral,Disagree,Disagree,Neutral,Neutral,Disagree,Agree,Agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Disagree,Disagree,Strongly disagree,Disagree,Neutral,Strongly disagree,Yes,Anytime (Mon-Sun),15 minutes,Agree,Neutral,Agree,Agree,Agree,Yes,Important,Very Important,Very Important,Important,Important,Very Important,Important,Important,No,NIL
+32,Female,Married,House wife,No Income,School,5,12.9889,77.5741,560020,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Strongly agree,Strongly agree,Agree,Agree,Agree,Agree,Disagree,Disagree,Disagree,Strongly disagree,Strongly disagree,Neutral,Disagree,Neutral,Disagree,Disagree,Disagree,Maybe,Weekend (Sat & Sun),60 minutes,Agree,Disagree,Neutral,Agree,Neutral,Yes,Important,Moderately Important,Moderately Important,Very Important,Important,Moderately Important,Moderately Important,Important,Yes,All good with the service.
+28,Male,Married,Employee,More than 50000,Post Graduate,1,12.9925,77.5633,560021,Food delivery apps, Walk-in,Breakfast, Lunch,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Strongly agree,Agree,Agree,Strongly agree,Disagree,Disagree,Disagree,Neutral,Disagree,Disagree,Disagree,Neutral,Neutral,Agree,Disagree,Disagree,Strongly disagree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Strongly Agree,Agree,Strongly Agree,Agree,Yes,Very Important,Very Important,Moderately Important,Important,Important,Slightly Important,Important,Slightly Important,Yes,"I love zomato's service 
+-from a premium member"
+22,Male,Single,Student,No Income,Post Graduate,2,12.977,77.5773,560009,Food delivery apps, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Agree,Agree,Agree,Agree,Neutral,Agree,Agree,Strongly agree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Strongly agree,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Agree,Agree,Agree,Agree,Yes,Important,Very Important,Moderately Important,Important,Important,Important,Very Important,Very Important,Yes,NIL
+25,Male,Single,Employee,10001 to 25000,Graduate,2,12.9757,77.5586,560023,Food delivery apps, Web browser,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Agree,Neutral,Disagree,Agree,Agree,Agree,Agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Disagree,Agree,Strongly disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),60 minutes,Agree,Strongly Agree,Agree,Agree,Strongly Agree,Yes,Important,Important,Very Important,Important,Important,Important,Very Important,Important,Yes,"I love swiggy service, followed by zomato"
+26,Female,Married,Employee,25001 to 50000,Post Graduate,2,12.9757,77.5586,560023,Food delivery apps, Web browser,Dinner, Dinner,Veg foods (Breakfast / Lunch / Dinner), Sweets,Disagree,Disagree,Disagree,Disagree,Neutral,Neutral,Disagree,Agree,Agree,Agree,Strongly agree,Agree,Disagree,Disagree,Agree,Neutral,Neutral,Disagree,Agree,Disagree,No,Weekend (Sat & Sun),30 minutes,Agree,Agree,Strongly Agree,Agree,Neutral,No,Slightly Important,Moderately Important,Moderately Important,Important,Important,Moderately Important,Moderately Important,Important,No,Food quality is reducing when it gets delivered
+31,Male,Married,Self Employeed,More than 50000,School,6,13.0487,77.5923,560024,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Disagree,Agree,Agree,Agree,Strongly agree,Agree,Agree,Agree,Agree,Agree,Agree,Disagree,Disagree,Agree,Agree,Strongly agree,Strongly disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Agree,Strongly Agree,Agree,Agree,Yes,Important,Important,Important,Important,Important,Important,Important,Important,Yes,My family is satisfied with food delivery. But I worry about the quality and hygiene
+24,Male,Single,Student,No Income,Post Graduate,3,13.0487,77.5923,560024,Food delivery apps, Direct call,Breakfast, Lunch,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Agree,Agree,Agree,Agree,Disagree,Disagree,Disagree,Disagree,Strongly disagree,Strongly disagree,Strongly disagree,Agree,Strongly agree,Agree,Strongly disagree,Disagree,Disagree,Maybe,Anytime (Mon-Sun),45 minutes,Neutral,Disagree,Neutral,Neutral,Disagree,No,Important,Important,Very Important,Important,Important,Moderately Important,Important,Important,No,I had bad experiences in delivered food
+24,Male,Single,Employee,25001 to 50000,Post Graduate,2,12.9662,77.6068,560025,Food delivery apps, Web browser,Snacks, Dinner,Veg foods (Breakfast / Lunch / Dinner), Sweets,Agree,Strongly agree,Agree,Strongly agree,Agree,Strongly agree,Agree,Disagree,Neutral,Disagree,Neutral,Disagree,Disagree,Neutral,Disagree,Neutral,Neutral,Neutral,Agree,Neutral,Yes,Anytime (Mon-Sun),30 minutes,Disagree,Disagree,Neutral,Agree,Agree,Yes,Important,Important,Important,Important,Moderately Important,Important,Important,Important,Yes,Marvelous service by swiggy and zomato
+31,Female,Married,Employee,More than 50000,Ph.D,5,12.9662,77.6068,560025,Food delivery apps, Walk-in,Breakfast, Lunch,Non Veg foods (Lunch / Dinner), Sweets,Agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Agree,Disagree,Neutral,Disagree,Neutral,Neutral,Disagree,Disagree,Neutral,Agree,Agree,Disagree,Disagree,Strongly disagree,Maybe,Weekdays (Mon-Fri),60 minutes,Agree,Neutral,Neutral,Disagree,Disagree,No,Moderately Important,Moderately Important,Slightly Important,Slightly Important,Important,Important,Moderately Important,Moderately Important,Yes,I don't bother much about delivery unless the food is good
+26,Male,Single,Employee,25001 to 50000,Graduate,2,12.9343,77.6044,560029,Food delivery apps, Direct call,Lunch, Dinner,Veg foods (Breakfast / Lunch / Dinner), Ice cream / Cool drinks,Disagree,Disagree,Neutral,Disagree,Strongly disagree,Disagree,Neutral,Agree,Agree,Strongly agree,Agree,Agree,Neutral,Strongly disagree,Agree,Agree,Neutral,Strongly disagree,Disagree,Agree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Agree,Strongly Agree,Agree,Agree,Maybe,Important,Moderately Important,Important,Important,Slightly Important,Moderately Important,Important,Important,Yes,Swiggy delivery people are polite and fast as well
+24,Female,Married,Self Employeed,More than 50000,Graduate,2,12.9343,77.6044,560029,Food delivery apps, Direct call,Breakfast, Lunch,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Neutral,Agree,Agree,Agree,Agree,Disagree,Disagree,Disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Agree,Agree,Strongly agree,Disagree,Disagree,Disagree,No,Anytime (Mon-Sun),More than 60 minutes,Agree,Agree,Strongly Agree,Agree,Agree,Yes,Moderately Important,Important,Moderately Important,Important,Very Important,Very Important,Very Important,Very Important,Yes,"Time doesn't bother me, But rating does. I order at high rated restaurants. preferably 4+ ratings"
+22,Female,Single,Student,No Income,Graduate,3,12.9343,77.6044,560029,Food delivery apps, Web browser,Breakfast, Snacks,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Neutral,Agree,Agree,Agree,Agree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Agree,Agree,Agree,Disagree,Disagree,Disagree,Yes,Weekend (Sat & Sun),30 minutes,Agree,Strongly Agree,Agree,Agree,Agree,Yes,Important,Important,Important,Important,Important,Very Important,Important,Important,Yes,NIL
+19,Male,Single,Student,No Income,Graduate,6,12.9442,77.6076,560030,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Agree,Disagree,Disagree,Neutral,Disagree,Agree,Disagree,Agree,Neutral,Neutral,Strongly disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),15 minutes,Agree,Agree,Strongly Agree,Agree,Strongly Agree,Yes,Important,Important,Very Important,Important,Important,Important,Important,Important,Yes,I have no trouble with delivery of startup services
+25,Male,Married,Employee,More than 50000,Post Graduate,6,12.9442,77.6076,560030,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Agree,Neutral,Agree,Neutral,Disagree,Agree,Agree,Disagree,Disagree,Agree,Disagree,Strongly disagree,Strongly disagree,Strongly disagree,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),45 minutes,Strongly Agree,Strongly Agree,Strongly Agree,Strongly Agree,Strongly Agree,Yes,Important,Important,Important,Very Important,Important,Important,Important,Important,Yes,My family prefers good quality food so I order at highly rated restaurants
+23,Female,Married,House wife,No Income,School,6,12.9442,77.6076,560030,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Agree,Agree,Strongly agree,Agree,Agree,Agree,Agree,Disagree,Disagree,Disagree,Disagree,Agree,Agree,Agree,Disagree,Disagree,Disagree,Maybe,Anytime (Mon-Sun),More than 60 minutes,Agree,Agree,Strongly Agree,Agree,Neutral,No,Moderately Important,Moderately Important,Slightly Important,Moderately Important,Important,Important,Very Important,Very Important,Yes,NIL
+23,Female,Single,Student,No Income,Graduate,2,13.0298,77.6047,560032,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Disagree,Strongly disagree,Disagree,Disagree,Disagree,Strongly disagree,Agree,Agree,Strongly agree,Agree,Strongly agree,Strongly agree,Agree,Agree,Agree,Agree,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Agree,Strongly Agree,Agree,Yes,Important,Important,Important,Moderately Important,Moderately Important,Slightly Important,Important,Important,No,I had bad experience with delivery. It could be due to location.
+23,Male,Single,Student,No Income,Post Graduate,2,12.9261,77.6221,560034,Food delivery apps, Web browser,Breakfast, Lunch,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Strongly agree,Agree,Agree,Strongly agree,Disagree,Disagree,Agree,Disagree,Neutral,Disagree,Disagree,Disagree,Neutral,Neutral,Strongly disagree,Strongly disagree,Strongly disagree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Strongly Agree,Agree,Agree,Agree,Yes,Important,Important,Very Important,Important,Important,Very Important,Important,Important,Yes,Brilliant execution with the delivery process
+24,Male,Single,Student,No Income,Post Graduate,5,12.9621,77.5376,560104,Food delivery apps, Direct call,Breakfast, Snacks,Veg foods (Breakfast / Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Strongly agree,Agree,Neutral,Neutral,Neutral,Neutral,Disagree,Agree,Disagree,Disagree,Disagree,Disagree,Strongly disagree,Disagree,Disagree,Disagree,Agree,Agree,Strongly disagree,Maybe,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Strongly Agree,Agree,Agree,Yes,Very Important,Important,Moderately Important,Slightly Important,Very Important,Important,Very Important,Very Important,Yes,Nil
+22,Female,Single,Employee,25001 to 50000,Graduate,4,12.8845,77.6036,560076,Food delivery apps, Walk-in,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Agree,Neutral,Neutral,Agree,Neutral,Agree,Agree,Strongly agree,Neutral,Strongly agree,Agree,Agree,Disagree,Disagree,Disagree,Disagree,Agree,Agree,Agree,Yes,Anytime (Mon-Sun),45 minutes,Strongly Agree,Strongly Agree,Strongly Agree,Agree,Agree,Yes,Important,Very Important,Very Important,Very Important,Very Important,Important,Very Important,Important,Yes,NIL
+26,Male,Married,Employee,More than 50000,Graduate,4,12.9048,77.6821,560036,Food delivery apps, Web browser,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Strongly agree,Disagree,Disagree,Strongly disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Agree,Disagree,Disagree,Strongly disagree,No,Anytime (Mon-Sun),45 minutes,Agree,Strongly Agree,Agree,Agree,Strongly Agree,Yes,Important,Important,Important,Important,Slightly Important,Moderately Important,Important,Important,Yes,"My home is near Phoenix marketcity, so time taken for delivery is less"
+25,Female,Single,Student,No Income,Ph.D,3,12.9048,77.6821,560036,Food delivery apps, Web browser,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Agree,Strongly agree,Agree,Strongly agree,Disagree,Disagree,Strongly disagree,Disagree,Strongly disagree,Strongly disagree,Strongly disagree,Disagree,Disagree,Agree,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Strongly Agree,Agree,Neutral,Agree,No,Slightly Important,Moderately Important,Slightly Important,Slightly Important,Important,Important,Important,Important,Yes,I'm quite satisfied with the service
+20,Male,Single,Student,No Income,Graduate,2,12.9261,77.6221,560034,Food delivery apps, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Strongly agree,Disagree,Disagree,Agree,Disagree,Disagree,Strongly disagree,Strongly disagree,Agree,Strongly agree,Agree,Strongly disagree,Strongly disagree,Strongly disagree,Yes,Weekend (Sat & Sun),45 minutes,Agree,Strongly Agree,Agree,Agree,Disagree,Yes,Important,Moderately Important,Moderately Important,Important,Important,Very Important,Very Important,Important,Yes,I had no issues as per my experience. But heard delivery people are sometimes rude
+29,Male,Married,Employee,25001 to 50000,Graduate,4,12.9261,77.6221,560034,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Agree,Agree,Disagree,Strongly disagree,Disagree,Strongly agree,Agree,Strongly agree,Disagree,Neutral,Agree,Strongly disagree,Strongly disagree,Disagree,Agree,Agree,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),15 minutes,Agree,Agree,Strongly Agree,Agree,Agree,Maybe,Important,Moderately Important,Important,Important,Very Important,Very Important,Very Important,Very Important,No,I had few health issues after consuming delivered food. Restaurants need to improve their quality and delivery people should delivery it without tampering which might lead to contamination of food
+23,Female,Single,Student,No Income,Graduate,1,12.977,77.5773,560009,Food delivery apps, Direct call,Breakfast, Lunch,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly disagree,Neutral,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Disagree,Disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Disagree,Strongly disagree,Strongly disagree,Strongly disagree,Yes,Anytime (Mon-Sun),15 minutes,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Disagree,No,Slightly Important,Unimportant,Unimportant,Unimportant,Unimportant,Slightly Important,Unimportant,Unimportant,Yes,NIL
+25,Male,Single,Self Employeed,More than 50000,Graduate,2,12.9783,77.6408,560038,Food delivery apps, Walk-in,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Agree,Agree,Neutral,Disagree,Disagree,Strongly disagree,Disagree,Disagree,Neutral,Agree,Disagree,Strongly disagree,Strongly disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Agree,Agree,Agree,Yes,Important,Important,Important,Very Important,Slightly Important,Moderately Important,Important,Very Important,Yes,I order most of my meals on swiggy or zomato depending upon the offers
+29,Female,Married,Employee,25001 to 50000,Graduate,4,12.9783,77.6408,560038,Food delivery apps, Walk-in,Lunch, Dinner,Veg foods (Breakfast / Lunch / Dinner), Sweets,Disagree,Neutral,Disagree,Neutral,Strongly disagree,Agree,Agree,Agree,Agree,Agree,Agree,Disagree,Disagree,Disagree,Disagree,Agree,Agree,Strongly disagree,Neutral,Agree,Yes,Anytime (Mon-Sun),15 minutes,Agree,Agree,Disagree,Agree,Neutral,Yes,Slightly Important,Moderately Important,Moderately Important,Very Important,Slightly Important,Moderately Important,Important,Important,No,I need the order to be delivered soon. It takes a lot of time 
+27,Male,Married,Self Employeed,25001 to 50000,Graduate,6,12.9217,77.5936,560041,Food delivery apps, Walk-in,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Disagree,Agree,Neutral,Agree,Agree,Strongly agree,Agree,Agree,Agree,Strongly agree,Agree,Disagree,Agree,Agree,Agree,Agree,Disagree,Disagree,Disagree,No,Anytime (Mon-Sun),60 minutes,Agree,Agree,Agree,Agree,Strongly Agree,Yes,Important,Important,Very Important,Very Important,Moderately Important,Important,Important,Important,No,Food needs to have good quality. It leads me to give higher rating
+25,Male,Single,Self Employeed,10001 to 25000,Graduate,3,13.0206,77.6479,560043,Food delivery apps, Walk-in,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Agree,Agree,Agree,Agree,Disagree,Disagree,Strongly disagree,Disagree,Disagree,Disagree,Disagree,Agree,Neutral,Agree,Neutral,Disagree,Disagree,No,Anytime (Mon-Sun),60 minutes,Agree,Strongly Agree,Agree,Agree,Agree,No,Moderately Important,Moderately Important,Important,Important,Slightly Important,Moderately Important,Important,Important,Yes,I appreciate the efforts of hardworking delivery people
+21,Male,Single,Student,No Income,Graduate,2,13.0012,77.5995,560046,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Disagree,Neutral,Disagree,Neutral,Disagree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Disagree,Neutral,Agree,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Agree,Agree,Strongly Agree,Yes,Moderately Important,Important,Important,Important,Important,Very Important,Very Important,Very Important,No,Delivery is late. Need quick updates on location of order
+23,Male,Single,Student,No Income,Graduate,3,13.0223,77.7132,560049,Food delivery apps, Web browser,Breakfast, Dinner,Veg foods (Breakfast / Lunch / Dinner), Sweets,Agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Strongly agree,Disagree,Disagree,Disagree,Neutral,Agree,Disagree,Disagree,Disagree,Neutral,Disagree,Agree,Disagree,Neutral,Yes,Anytime (Mon-Sun),30 minutes,Agree,Neutral,Agree,Agree,Neutral,Yes,Important,Important,Very Important,Important,Slightly Important,Moderately Important,Important,Very Important,Yes,I have no trouble with delivery
+24,Female,Single,Employee,10001 to 25000,Post Graduate,4,12.9337,77.59,560011,Food delivery apps, Direct call,Snacks, Dinner,Veg foods (Breakfast / Lunch / Dinner), Ice cream / Cool drinks,Agree,Agree,Agree,Neutral,Disagree,Neutral,Neutral,Agree,Agree,Neutral,Neutral,Neutral,Disagree,Neutral,Neutral,Agree,Neutral,Neutral,Neutral,Neutral,Yes,Anytime (Mon-Sun),45 minutes,Neutral,Neutral,Agree,Agree,Agree,Yes,Important,Moderately Important,Moderately Important,Important,Important,Important,Important,Important,Yes,Nil
+32,Male,Married,Self Employeed,10001 to 25000,School,3,12.982,77.6256,560008,Food delivery apps, Walk-in,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Strongly agree,Neutral,Neutral,Strongly agree,Disagree,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Yes,Weekend (Sat & Sun),45 minutes,Neutral,Neutral,Neutral,Disagree,Neutral,No,Unimportant,Slightly Important,Unimportant,Unimportant,Very Important,Very Important,Very Important,Very Important,Yes,Very few times I have  had bad experienced by aggregator or restaurant 
+28,Male,Married,Employee,25001 to 50000,Post Graduate,5,13.0262,77.62,560045,Food delivery apps, Walk-in,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Agree,Agree,Neutral,Neutral,Disagree,Disagree,Agree,Disagree,Agree,Agree,Neutral,Disagree,Agree,Agree,Neutral,Disagree,Neutral,Yes,Anytime (Mon-Sun),45 minutes,Agree,Agree,Disagree,Disagree,Neutral,No,Moderately Important,Slightly Important,Moderately Important,Important,Moderately Important,Moderately Important,Important,Important,Yes,Best service comes with great offers and wonderful service
+26,Male,Single,Employee,10001 to 25000,Graduate,2,12.9217,77.5936,560041,Food delivery apps, Walk-in,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Neutral,Neutral,Agree,Strongly agree,Disagree,Disagree,Neutral,Neutral,Disagree,Neutral,Agree,Agree,Neutral,Agree,Neutral,Neutral,Agree,Yes,Weekend (Sat & Sun),30 minutes,Agree,Agree,Strongly Agree,Neutral,Agree,Yes,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,No,Bad delivery system and less restaurant choices
+31,Female,Married,House wife,No Income,Graduate,5,13.0078,77.5577,560055,Food delivery apps, Walk-in,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Agree,Neutral,Agree,Agree,Agree,Agree,Disagree,Neutral,Disagree,Disagree,Disagree,Agree,Neutral,Neutral,Strongly disagree,Disagree,Neutral,No,Anytime (Mon-Sun),60 minutes,Agree,Agree,Agree,Neutral,Disagree,Yes,Important,Moderately Important,Moderately Important,Important,Important,Moderately Important,Moderately Important,Important,Yes,I order most of the times online and I feel they are safe and quick
+27,Female,Married,Self Employeed,25001 to 50000,Graduate,3,13.0078,77.5577,560055,Food delivery apps, Web browser,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Agree,Agree,Neutral,Agree,Disagree,Disagree,Neutral,Disagree,Neutral,Disagree,Neutral,Agree,Neutral,Agree,Neutral,Agree,Neutral,Yes,Anytime (Mon-Sun),45 minutes,Agree,Neutral,Agree,Agree,Neutral,No,Important,Moderately Important,Moderately Important,Slightly Important,Important,Moderately Important,Important,Important,Yes,I don't bother about ratings unless they taste good. But I prefer quicker delivery. Can't hold my hunger 
+21,Female,Single,Student,No Income,Post Graduate,1,12.9217,77.5936,560041,Food delivery apps, Walk-in,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Neutral,Strongly agree,Neutral,Neutral,Agree,Strongly disagree,Disagree,Neutral,Neutral,Agree,Agree,Neutral,Strongly agree,Agree,Agree,Agree,Neutral,Neutral,Maybe,Weekend (Sat & Sun),30 minutes,Neutral,Neutral,Neutral,Agree,Neutral,Maybe,Important,Very Important,Important,Moderately Important,Very Important,Important,Very Important,Important,Yes,Good
+23,Female,Single,Student,No Income,Graduate,2,12.9105,77.4842,560060,Food delivery apps, Web browser,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly disagree,Disagree,Disagree,Disagree,Agree,Neutral,Disagree,Neutral,Agree,Agree,Neutral,Disagree,Disagree,Strongly agree,Strongly agree,Strongly agree,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),45 minutes,Disagree,Disagree,Disagree,Neutral,Agree,Yes,Important,Moderately Important,Important,Important,Slightly Important,Important,Important,Moderately Important,No,I dont have much restaurant choices and takes long time to deliver
+26,Male,Married,Employee,10001 to 25000,Graduate,4,12.9105,77.4842,560060,Food delivery apps, Walk-in,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Disagree,Disagree,Disagree,Neutral,Agree,Agree,Disagree,Disagree,Agree,Neutral,Disagree,Agree,Disagree,Agree,Neutral,Agree,Strongly disagree,Agree,Agree,Yes,Anytime (Mon-Sun),45 minutes,Disagree,Disagree,Agree,Disagree,Neutral,Yes,Important,Moderately Important,Moderately Important,Important,Slightly Important,Moderately Important,Moderately Important,Slightly Important,No,"My location is outside Bangalore, good restaurants are in interior regions"
+32,Male,Married,Employee,More than 50000,Graduate,5,12.9037,77.5376,560061,Web browser, Walk-in,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Neutral,Neutral,Agree,Agree,Agree,Disagree,Disagree,Agree,Neutral,Neutral,Neutral,Disagree,Disagree,Neutral,Neutral,Disagree,Neutral,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Neutral,Neutral,Agree,Neutral,No,Important,Moderately Important,Slightly Important,Slightly Important,Slightly Important,Moderately Important,Moderately Important,Moderately Important,Yes,Smooth delivery service 
+25,Female,Married,Student,No Income,Post Graduate,2,12.8834,77.5486,560062,Food delivery apps, Web browser,Snacks, Dinner,Sweets, Ice cream / Cool drinks,Agree,Disagree,Agree,Neutral,Neutral,Agree,Agree,Disagree,Neutral,Agree,Disagree,Disagree,Strongly disagree,Strongly disagree,Strongly agree,Strongly agree,Strongly agree,Disagree,Agree,Agree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Neutral,Agree,Neutral,Neutral,Yes,Important,Moderately Important,Important,Moderately Important,Slightly Important,Moderately Important,Important,Very Important,Yes,Fast delivery is required. But quality is super good
+28,Male,Single,Self Employeed,10001 to 25000,Post Graduate,2,12.9149,77.5635,560070,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Neutral,Agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Agree,Disagree,Agree,Disagree,Disagree,Strongly disagree,Strongly disagree,Disagree,Disagree,Disagree,Strongly disagree,Strongly disagree,Strongly disagree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Neutral,Neutral,Agree,Neutral,Maybe,Moderately Important,Slightly Important,Slightly Important,Moderately Important,Slightly Important,Important,Very Important,Very Important,Yes,LOVE SWIGGY AND ZOMATO. Literally the best
+21,Female,Single,Student,No Income,Post Graduate,3,12.9149,77.5635,560070,Food delivery apps, Walk-in,Breakfast, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Neutral,Agree,Neutral,Agree,Neutral,Neutral,Agree,Agree,Disagree,Disagree,Disagree,Disagree,Disagree,Agree,Disagree,Neutral,Neutral,Disagree,Neutral,Maybe,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Agree,Neutral,Agree,Yes,Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Yes,The delivery to my locations are done pretty fast
+24,Male,Single,Student,No Income,Post Graduate,2,12.9706,77.6529,560075,Food delivery apps, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Agree,Neutral,Agree,Strongly agree,Disagree,Disagree,Agree,Disagree,Disagree,Neutral,Disagree,Agree,Neutral,Neutral,Disagree,Disagree,Strongly disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Neutral,Agree,Neutral,Disagree,Yes,Important,Slightly Important,Important,Moderately Important,Moderately Important,Important,Important,Very Important,Yes,NIL
+26,Female,Married,Self Employeed,10001 to 25000,Graduate,5,12.9706,77.6529,560075,Food delivery apps, Direct call,Dinner, Dinner,Veg foods (Breakfast / Lunch / Dinner), Sweets,Disagree,Disagree,Neutral,Agree,Disagree,Neutral,Agree,Agree,Agree,Agree,Neutral,Neutral,Agree,Agree,Disagree,Neutral,Neutral,Disagree,Disagree,Disagree,Maybe,Anytime (Mon-Sun),45 minutes,Agree,Agree,Neutral,Disagree,Disagree,Maybe,Important,Important,Very Important,Important,Important,Important,Important,Important,No,I require the food to be undamaged. Mostly the food is tampered
+32,Male,Married,Employee,25001 to 50000,Graduate,3,12.9706,77.6529,560075,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Strongly agree,Agree,Neutral,Disagree,Disagree,Disagree,Neutral,Agree,Agree,Neutral,Disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Neutral,Agree,Agree,Strongly Agree,Yes,Important,Moderately Important,Important,Moderately Important,Important,Important,Important,Important,Yes,Best Best Best Best !!!!
+29,Male,Single,Self Employeed,More than 50000,Graduate,6,12.8845,77.6036,560076,Food delivery apps, Web browser,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Neutral,Disagree,Agree,Agree,Agree,Disagree,Disagree,Disagree,Neutral,Disagree,Disagree,Strongly disagree,Agree,Neutral,Agree,Disagree,Disagree,Disagree,Yes,Weekend (Sat & Sun),More than 60 minutes,Agree,Neutral,Agree,Neutral,Agree,No,Moderately Important,Slightly Important,Moderately Important,Slightly Important,Slightly Important,Moderately Important,Moderately Important,Slightly Important,Yes,Love to order online 
+21,Male,Single,Student,No Income,Graduate,5,12.9783,77.6408,560038,Food delivery apps, Web browser,Lunch, Snacks,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Neutral,Agree,Neutral,Agree,Agree,Disagree,Disagree,Disagree,Disagree,Neutral,Disagree,Agree,Agree,Strongly agree,Yes,Weekend (Sat & Sun),45 minutes,Agree,Neutral,Agree,Strongly Agree,Strongly Agree,Yes,Important,Important,Very Important,Important,Slightly Important,Moderately Important,Important,Important,Yes,I love ordering for my family from swiggy
+24,Female,Single,Self Employeed,25001 to 50000,Post Graduate,3,13.0103,77.5796,560080,Food delivery apps, Direct call,Dinner, Dinner,Veg foods (Breakfast / Lunch / Dinner), Sweets,Disagree,Disagree,Disagree,Neutral,Agree,Agree,Agree,Agree,Disagree,Disagree,Disagree,Disagree,Neutral,Agree,Agree,Neutral,Agree,Disagree,Disagree,Strongly disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Neutral,Neutral,Agree,Neutral,Maybe,Important,Important,Important,Moderately Important,Moderately Important,Important,Important,Important,No,I need faster delivery.
+26,Male,Prefer not to say,Self Employeed,More than 50000,Post Graduate,2,13.0103,77.5796,560080,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Strongly agree,Neutral,Strongly agree,Agree,Agree,Disagree,Disagree,Disagree,Disagree,Disagree,Agree,Disagree,Disagree,Agree,Agree,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Agree,Strongly Agree,Agree,Agree,No,Moderately Important,Moderately Important,Slightly Important,Slightly Important,Slightly Important,Slightly Important,Moderately Important,Moderately Important,Yes,Crazily fast delivery people besides having high traffic
+25,Male,Married,Employee,More than 50000,Ph.D,3,12.9306,77.5434,560085,Food delivery apps, Web browser,Lunch, Snacks,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Neutral,Agree,Agree,Neutral,Agree,Agree,Neutral,Disagree,Disagree,Disagree,Neutral,Disagree,Disagree,Disagree,Disagree,Disagree,Neutral,Disagree,Disagree,No,Anytime (Mon-Sun),30 minutes,Agree,Neutral,Agree,Neutral,Disagree,Yes,Important,Important,Very Important,Important,Moderately Important,Important,Important,Moderately Important,Yes,BEST BEST BEST !! WONDERFUL WONDERFUL !! FAST FAST FAST !!
+29,Male,Single,Employee,25001 to 50000,Graduate,3,13.0641,77.5931,560092,Food delivery apps, Web browser,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Disagree,Disagree,Strongly disagree,Disagree,Disagree,Strongly disagree,Agree,Neutral,Agree,Neutral,Agree,Neutral,Agree,Neutral,Agree,Agree,Strongly disagree,Strongly disagree,Strongly disagree,Yes,Anytime (Mon-Sun),45 minutes,Neutral,Agree,Agree,Agree,Strongly Agree,Yes,Important,Very Important,Important,Very Important,Moderately Important,Important,Important,Very Important,No,Need quality food delivery. had worst experience of spilled food
+22,Male,Single,Student,No Income,Graduate,3,13.0158,77.539,560096,Food delivery apps, Web browser,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Neutral,Neutral,Agree,Strongly agree,Agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Strongly agree,Disagree,Disagree,Neutral,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Agree,Neutral,Agree,Yes,Important,Very Important,Important,Very Important,Important,Very Important,Very Important,Important,Yes,NIL
+24,Male,Single,Student,No Income,Graduate,2,12.9561,77.5921,560027,Direct call, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Strongly agree,Neutral,Neutral,Neutral,Neutral,Neutral,Strongly disagree,Disagree,Neutral,Disagree,Disagree,Neutral,Disagree,Agree,Agree,Agree,Agree,Strongly agree,Neutral,Yes,Weekend (Sat & Sun),30 minutes,Agree,Agree,Agree,Neutral,Neutral,Yes,Important,Important,Moderately Important,Important,Very Important,Very Important,Very Important,Very Important,Yes,NIL
+27,Male,Married,Employee,25001 to 50000,Graduate,2,12.8845,77.6036,560076,Food delivery apps, Web browser,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Disagree,Agree,Neutral,Strongly agree,Neutral,Agree,Disagree,Disagree,Agree,Disagree,Disagree,Disagree,Neutral,Agree,Neutral,Agree,Strongly agree,Agree,Neutral,No,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Agree,Agree,Strongly Agree,Yes,Important,Moderately Important,Important,Moderately Important,Moderately Important,Important,Moderately Important,Moderately Important,Yes,I would avoid restaurants which has less rating 
+23,Female,Single,Student,No Income,Post Graduate,3,12.9369,77.6407,560095,Food delivery apps, Web browser,Snacks, Dinner,Veg foods (Breakfast / Lunch / Dinner), Sweets,Disagree,Neutral,Neutral,Disagree,Disagree,Neutral,Strongly disagree,Agree,Agree,Agree,Agree,Neutral,Disagree,Disagree,Disagree,Neutral,Agree,Disagree,Strongly disagree,Strongly disagree,Yes,Anytime (Mon-Sun),15 minutes,Agree,Agree,Strongly Agree,Agree,Strongly Agree,Maybe,Important,Important,Very Important,Important,Moderately Important,Moderately Important,Important,Important,No,Faster delivery is needed !!
+32,Male,Married,Employee,More than 50000,Post Graduate,6,12.9369,77.6407,560095,Food delivery apps, Walk-in,Lunch, Snacks,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Disagree,Neutral,Neutral,Agree,Neutral,Agree,Agree,Disagree,Neutral,Disagree,Disagree,Disagree,Agree,Neutral,Agree,Disagree,Disagree,Disagree,Yes,Weekend (Sat & Sun),45 minutes,Agree,Neutral,Agree,Agree,Strongly Agree,Yes,Important,Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Yes,My entire family loves swiggy
+22,Female,Single,Student,No Income,Graduate,2,12.9369,77.6407,560095,Food delivery apps, Direct call,Breakfast, Snacks,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Disagree,Disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,No,Anytime (Mon-Sun),More than 60 minutes,Agree,Agree,Strongly Agree,Agree,Disagree,Maybe,Important,Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Yes,Delivery people are working hard. Can't complain
+28,Male,Married,Employee,25001 to 50000,Graduate,3,12.9369,77.6407,560095,Food delivery apps, Web browser,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Agree,Agree,Agree,Strongly agree,Disagree,Disagree,Strongly disagree,Disagree,Disagree,Strongly disagree,Strongly disagree,Disagree,Agree,Neutral,Disagree,Disagree,Disagree,Maybe,Weekend (Sat & Sun),60 minutes,Agree,Strongly Agree,Agree,Agree,Strongly Agree,Yes,Very Important,Very Important,Very Important,Very Important,Important,Important,Important,Very Important,Yes,I don't mind about late delivery when it comes from a top rated restaurant believing in its quality
+23,Female,Single,Student,No Income,Post Graduate,2,13.0158,77.539,560096,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Neutral,Agree,Strongly agree,Agree,Agree,Strongly agree,Neutral,Neutral,Neutral,Disagree,Disagree,Disagree,Disagree,Disagree,Strongly disagree,Strongly disagree,Disagree,Disagree,Strongly disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Agree,Strongly Agree,Agree,Agree,Yes,Important,Important,Moderately Important,Important,Moderately Important,Important,Important,Important,Yes,I have no issues related to delivery and I prefer four plus rated restaurant
+30,Male,Married,Self Employeed,More than 50000,Graduate,1,13.0809,77.5565,560097,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Neutral,Disagree,Disagree,Disagree,Disagree,Agree,Agree,Disagree,Agree,Agree,Disagree,Agree,Disagree,Neutral,Agree,Neutral,Agree,Agree,Disagree,Disagree,Yes,Anytime (Mon-Sun),15 minutes,Agree,Agree,Strongly Agree,Agree,Agree,Yes,Important,Important,Very Important,Important,Important,Important,Moderately Important,Important,No,I had misplaced order in the past orders
+21,Male,Single,Student,No Income,Graduate,3,13.0641,77.5931,560092,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Agree,Neutral,Agree,Neutral,Disagree,Disagree,Disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Disagree,Neutral,Agree,Disagree,Disagree,Strongly disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Agree,Strongly Agree,Agree,Agree,Yes,Important,Important,Important,Important,Important,Important,Important,Important,Yes,Best service
+26,Female,Married,Employee,Below Rs.10000,Graduate,6,12.9859,77.6713,560093,Food delivery apps, Direct call,Dinner, Dinner,Veg foods (Breakfast / Lunch / Dinner), Sweets,Disagree,Disagree,Disagree,Strongly disagree,Strongly disagree,Disagree,Agree,Agree,Agree,Agree,Strongly agree,Agree,Agree,Disagree,Agree,Neutral,Agree,Agree,Disagree,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Neutral,Agree,Neutral,Agree,Yes,Important,Moderately Important,Important,Important,Important,Very Important,Very Important,Very Important,No,I had bad quality order delivered twice
+25,Male,Single,Self Employeed,More than 50000,School,3,12.9859,77.6713,560093,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Agree,Strongly agree,Strongly agree,Strongly agree,Agree,Disagree,Strongly disagree,Agree,Disagree,Strongly disagree,Disagree,Agree,Agree,Agree,Neutral,Disagree,Disagree,Maybe,Anytime (Mon-Sun),60 minutes,Agree,Disagree,Disagree,Agree,Disagree,No,Moderately Important,Slightly Important,Slightly Important,Slightly Important,Slightly Important,Slightly Important,Unimportant,Slightly Important,Yes,Bad rating doesn't mean that the food tastes bad. Delivery got no problem !
+31,Male,Prefer not to say,Employee,Below Rs.10000,Graduate,1,12.9866,77.4904,560091,Food delivery apps, Direct call,Lunch, Snacks,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Neutral,Disagree,Disagree,Disagree,Disagree,Agree,Agree,Agree,Agree,Agree,Neutral,Disagree,Disagree,Strongly agree,Agree,Agree,Agree,Disagree,Disagree,Disagree,Yes,Weekend (Sat & Sun),15 minutes,Agree,Disagree,Disagree,Neutral,Agree,Yes,Important,Moderately Important,Important,Very Important,Very Important,Very Important,Very Important,Very Important,No,Order delivered to my location are late
+23,Female,Single,Employee,25001 to 50000,Post Graduate,2,12.9847,77.5491,560100,Food delivery apps, Walk-in,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Neutral,Agree,Agree,Strongly agree,Agree,Strongly agree,Disagree,Disagree,Strongly disagree,Disagree,Strongly disagree,Strongly disagree,Disagree,Agree,Agree,Neutral,Agree,Agree,Strongly agree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Strongly Agree,Agree,Agree,Yes,Important,Moderately Important,Important,Important,Important,Important,Very Important,Very Important,Yes,My location is pretty well built for food delivery 
+29,Female,Married,Employee,25001 to 50000,Ph.D,3,12.9847,77.5491,560100,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Agree,Strongly agree,Strongly agree,Strongly agree,Disagree,Disagree,Disagree,Strongly disagree,Agree,Strongly disagree,Strongly disagree,Agree,Agree,Strongly agree,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Strongly Agree,Agree,Agree,Strongly Agree,Yes,Important,Important,Very Important,Important,Very Important,Very Important,Very Important,Very Important,Yes,I believe more on quality than time of delivery
+21,Male,Single,Student,No Income,Graduate,6,12.9847,77.5491,560100,Food delivery apps, Direct call,Breakfast, Lunch,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Disagree,Disagree,Strongly agree,Agree,Agree,Disagree,Disagree,Agree,Agree,Strongly agree,Agree,Strongly agree,Strongly agree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Agree,Strongly Agree,Strongly Agree,Yes,Slightly Important,Slightly Important,Moderately Important,Slightly Important,Important,Moderately Important,Slightly Important,Slightly Important,Yes,NIL
+20,Male,Single,Student,No Income,Graduate,3,12.9299,77.6848,560103,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Agree,Strongly agree,Agree,Agree,Disagree,Disagree,Agree,Disagree,Disagree,Neutral,Strongly disagree,Agree,Neutral,Disagree,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Strongly Agree,Agree,Strongly Agree,Strongly Agree,Yes,Important,Important,Important,Important,Important,Important,Important,Important,Yes,Super fast riders helps in super fast delivery
+22,Male,Single,Employee,10001 to 25000,Graduate,2,12.9299,77.6848,560103,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Agree,Strongly agree,Strongly agree,Strongly agree,Disagree,Disagree,Strongly disagree,Disagree,Strongly disagree,Disagree,Strongly disagree,Agree,Agree,Agree,Disagree,Strongly disagree,Strongly disagree,Maybe,Anytime (Mon-Sun),More than 60 minutes,Agree,Agree,Strongly Agree,Disagree,Disagree,No,Slightly Important,Slightly Important,Slightly Important,Slightly Important,Slightly Important,Slightly Important,Very Important,Very Important,Yes,I believe rating doesn't have much effect on the taste of food
+30,Female,Married,House wife,No Income,School,6,12.9828,77.6131,560042,Food delivery apps, Web browser,Lunch, Snacks,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Disagree,Disagree,Strongly disagree,Disagree,Strongly disagree,Agree,Agree,Agree,Strongly agree,Agree,Agree,Agree,Agree,Strongly agree,Agree,Agree,Disagree,Neutral,Neutral,Yes,Anytime (Mon-Sun),30 minutes,Disagree,Disagree,Disagree,Disagree,Strongly disagree,Yes,Important,Moderately Important,Very Important,Moderately Important,Important,Very Important,Very Important,Very Important,No,I need quicker delivery and more extra restaurant choices
+27,Male,Married,Self Employeed,More than 50000,Graduate,3,12.989,77.5332,560079,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Neutral,Agree,Agree,Strongly agree,Strongly agree,Strongly agree,Disagree,Neutral,Agree,Strongly agree,Disagree,Disagree,Strongly disagree,Agree,Agree,Strongly agree,Disagree,Disagree,Disagree,No,Anytime (Mon-Sun),More than 60 minutes,Disagree,Agree,Neutral,Agree,Agree,Yes,Important,Important,Important,Important,Very Important,Important,Very Important,Very Important,Yes,Best best fast fast quick quick
+23,Male,Single,Student,No Income,Post Graduate,2,12.977,77.5773,560009,Food delivery apps, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Agree,Agree,Strongly agree,Agree,Disagree,Disagree,Agree,Disagree,Strongly disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Yes,Weekend (Sat & Sun),45 minutes,Agree,Strongly Agree,Disagree,Agree,Strongly Agree,Yes,Very Important,Very Important,Very Important,Very Important,Important,Important,Important,Important,No,My order rarely gets late. But it really pissess me off
+30,Male,Married,Self Employeed,25001 to 50000,School,6,12.9251,77.4992,560059,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Disagree,Neutral,Disagree,Disagree,Agree,Disagree,Agree,Agree,Agree,Agree,Disagree,Agree,Disagree,Disagree,Agree,Agree,Strongly agree,Strongly agree,Strongly agree,Yes,Weekend (Sat & Sun),45 minutes,Agree,Neutral,Agree,Agree,Agree,Yes,Important,Important,Important,Important,Very Important,Important,Very Important,Very Important,No,worst worst worst slow slow slow
+23,Male,Single,Student,No Income,Post Graduate,3,12.9967,77.7582,560067,Food delivery apps, Web browser,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Strongly agree,Strongly agree,Strongly agree,Agree,Disagree,Disagree,Agree,Agree,Disagree,Disagree,Disagree,Agree,Agree,Strongly agree,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Agree,Agree,Agree,Maybe,Very Important,Very Important,Very Important,Very Important,Important,Very Important,Important,Very Important,Yes,I'm satisfied with the service of swiggy
+28,Female,Married,Employee,25001 to 50000,Post Graduate,6,12.9967,77.7582,560067,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Disagree,Neutral,Disagree,Strongly disagree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Neutral,Agree,Neutral,Agree,Agree,Neutral,Agree,Agree,Yes,Anytime (Mon-Sun),15 minutes,Agree,Neutral,Disagree,Agree,Agree,Yes,Important,Important,Important,Important,Moderately Important,Important,Important,Very Important,No,Worst delivery system- Especially zomato 
+30,Male,Married,Self Employeed,More than 50000,Graduate,6,12.9967,77.7582,560067,Food delivery apps, Direct call,Lunch, Dinner,Veg foods (Breakfast / Lunch / Dinner), Sweets,Agree,Agree,Strongly agree,Agree,Neutral,Neutral,Agree,Disagree,Disagree,Neutral,Disagree,Neutral,Disagree,Strongly disagree,Agree,Agree,Disagree,Disagree,Disagree,Disagree,Maybe,Anytime (Mon-Sun),60 minutes,Agree,Agree,Strongly Agree,Agree,Agree,Yes,Important,Moderately Important,Slightly Important,Moderately Important,Important,Important,Important,Important,Yes,Whitefield got no problem with respect to delivery and we have a good set of restaurant here
+24,Female,Married,Employee,25001 to 50000,Post Graduate,2,12.957,77.5637,560018,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Neutral,Neutral,Disagree,Agree,Agree,Disagree,Disagree,Strongly disagree,Disagree,Disagree,Agree,Disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Neutral,No,Anytime (Mon-Sun),More than 60 minutes,Disagree,Disagree,Disagree,Strongly disagree,Disagree,Yes,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Yes,I can wait for quality food from highly rated restaurant
+21,Male,Single,Student,No Income,Graduate,2,12.957,77.5637,560018,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Disagree,Strongly disagree,Disagree,Disagree,Disagree,Strongly disagree,Disagree,Strongly disagree,Agree,Disagree,Disagree,Neutral,Agree,Disagree,Strongly disagree,Disagree,Strongly disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Disagree,Agree,Disagree,Neutral,Disagree,Yes,Important,Important,Moderately Important,Important,Moderately Important,Moderately Important,Important,Important,No,If it is from a low rated restaurant. I restrict myself to order from that restaurant
+23,Female,Prefer not to say,Employee,10001 to 25000,Graduate,3,12.9889,77.5741,560020,Food delivery apps, Direct call,Lunch, Snacks,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Agree,Strongly agree,Agree,Strongly agree,Agree,Agree,Neutral,Disagree,Disagree,Disagree,Disagree,Disagree,Agree,Disagree,Strongly disagree,Strongly disagree,Strongly disagree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Agree,Strongly Agree,Agree,Strongly Agree,Maybe,Important,Important,Slightly Important,Important,Slightly Important,Moderately Important,Important,Important,No,It serves my hunger on time. But there are times it delays when I needed the most
+25,Male,Married,Self Employeed,More than 50000,Post Graduate,4,13.0206,77.6479,560043,Food delivery apps, Walk-in,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Agree,Strongly agree,Strongly agree,Strongly agree,Disagree,Disagree,Agree,Disagree,Agree,Agree,Disagree,Disagree,Disagree,Disagree,Strongly disagree,Strongly disagree,Strongly disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Agree,Strongly Agree,Agree,Strongly Agree,Yes,Important,Important,Important,Important,Very Important,Very Important,Very Important,Very Important,Yes,I'm conscious about quality more than time of delivery
+24,Female,Single,Employee,25001 to 50000,Graduate,2,12.8893,77.6399,560068,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Agree,Strongly agree,Agree,Strongly agree,Disagree,Neutral,Agree,Neutral,Neutral,Disagree,Disagree,Agree,Agree,Agree,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Agree,Strongly Agree,Agree,Agree,Yes,Important,Important,Moderately Important,Important,Slightly Important,Moderately Important,Important,Very Important,Yes,LOVE SWIGGY AND ZOMATO
+26,Male,Married,Employee,10001 to 25000,Graduate,3,12.8893,77.6399,560068,Food delivery apps, Direct call,Lunch, Snacks,Sweets, Ice cream / Cool drinks,Disagree,Disagree,Strongly disagree,Strongly disagree,Disagree,Strongly disagree,Disagree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Yes,Anytime (Mon-Sun),15 minutes,Agree,Agree,Strongly Agree,Agree,Disagree,Yes,Important,Important,Very Important,Very Important,Important,Very Important,Very Important,Very Important,No,Poor delivery services
+25,Female,Single,Employee,25001 to 50000,Post Graduate,2,12.9967,77.7582,560067,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Agree,Agree,Strongly agree,Agree,Disagree,Neutral,Agree,Disagree,Strongly disagree,Disagree,Agree,Disagree,Disagree,Strongly disagree,Disagree,Neutral,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Agree,Strongly Agree,Agree,Agree,Maybe,Slightly Important,Slightly Important,Moderately Important,Slightly Important,Important,Moderately Important,Moderately Important,Moderately Important,Yes,Quick and fastest delivery made to my location
+22,Male,Single,Student,25001 to 50000,Graduate,3,12.9783,77.6408,560038,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Agree,Disagree,Agree,Neutral,Agree,Agree,Disagree,Neutral,Neutral,Agree,Neutral,Disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Disagree,No,Anytime (Mon-Sun),60 minutes,Agree,Agree,Strongly Agree,Neutral,Neutral,Yes,Important,Very Important,Slightly Important,Moderately Important,Slightly Important,Moderately Important,Slightly Important,Very Important,Yes,No issues
+26,Female,Single,Employee,25001 to 50000,Post Graduate,2,12.9783,77.6408,560038,Food delivery apps, Direct call,Breakfast, Lunch,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Neutral,Agree,Strongly agree,Strongly agree,Agree,Disagree,Disagree,Neutral,Agree,Disagree,Disagree,Disagree,Neutral,Neutral,Disagree,Neutral,Disagree,Neutral,Yes,Anytime (Mon-Sun),More than 60 minutes,Agree,Agree,Agree,Neutral,Disagree,Yes,Important,Important,Very Important,Important,Important,Important,Very Important,Important,Yes,Neat package by swiggy and also takes carry of quality during covid situation
+23,Male,Single,Employee,10001 to 25000,Graduate,2,12.9925,77.5633,560021,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Disagree,Strongly disagree,Disagree,Disagree,Neutral,Disagree,Agree,Agree,Agree,Agree,Neutral,Neutral,Disagree,Strongly agree,Agree,Neutral,Disagree,Disagree,Strongly disagree,Yes,Weekdays (Mon-Fri),15 minutes,Agree,Neutral,Agree,Neutral,Disagree,Yes,Important,Moderately Important,Moderately Important,Important,Important,Moderately Important,Important,Moderately Important,Yes,NIL
+21,Female,Single,Employee,Below Rs.10000,Graduate,2,12.9925,77.5633,560021,Food delivery apps, Walk-in,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Disagree,Strongly disagree,Disagree,Strongly disagree,Strongly disagree,Strongly disagree,Disagree,Disagree,Neutral,Agree,Agree,Agree,Agree,Disagree,Neutral,Agree,Agree,Strongly agree,Agree,Yes,Weekend (Sat & Sun),30 minutes,Agree,Neutral,Agree,Agree,Strongly Agree,Yes,Important,Important,Very Important,Important,Very Important,Very Important,Very Important,Very Important,No,very late deliveries to my house everytime i order
+25,Male,Married,Self Employeed,25001 to 50000,Graduate,3,12.9561,77.5921,560027,Food delivery apps, Direct call,Lunch, Snacks,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Neutral,Agree,Strongly agree,Agree,Neutral,Agree,Neutral,Disagree,Disagree,Neutral,Agree,Disagree,Disagree,Agree,Neutral,Neutral,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Agree,Strongly Agree,Agree,Strongly Agree,Yes,Important,Important,Moderately Important,Important,Important,Very Important,Very Important,Important,Yes,Love Swiggy's delivery men. They are lightning fast
+24,Female,Married,Employee,10001 to 25000,Post Graduate,2,12.9561,77.5921,560027,Food delivery apps, Direct call,Breakfast, Lunch,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Agree,Neutral,Agree,Strongly agree,Agree,Neutral,Neutral,Disagree,Disagree,Disagree,Disagree,Disagree,Neutral,Disagree,Disagree,Strongly disagree,Strongly disagree,No,Anytime (Mon-Sun),60 minutes,Agree,Strongly Agree,Agree,Agree,Strongly Agree,Yes,Important,Important,Very Important,Important,Important,Important,Important,Important,Yes,560075
+22,Male,Married,Student,No Income,Graduate,2,13.0734,77.5464,560014,Food delivery apps, Direct call,Snacks, Dinner,Veg foods (Breakfast / Lunch / Dinner), Sweets,Agree,Agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Agree,Disagree,Disagree,Disagree,Disagree,Disagree,Neutral,Disagree,Strongly agree,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Agree,Neutral,Agree,Yes,Important,Important,Important,Very Important,Very Important,Very Important,Very Important,Very Important,Yes,Quality is more important than time
+23,Female,Single,Student,No Income,Post Graduate,4,13.0487,77.5923,560024,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Strongly agree,Strongly agree,Neutral,Neutral,Strongly agree,Strongly agree,Neutral,Disagree,Neutral,Strongly agree,Agree,Neutral,Disagree,Agree,Agree,Agree,Strongly agree,Agree,Neutral,Yes,Weekend (Sat & Sun),45 minutes,Neutral,Neutral,Neutral,Neutral,Neutral,Yes,Important,Important,Moderately Important,Moderately Important,Very Important,Very Important,Very Important,Very Important,Yes,Nil
+24,Male,Married,Employee,More than 50000,Post Graduate,3,12.9515,77.4921,560056,Food delivery apps, Direct call,Snacks, Dinner,Veg foods (Breakfast / Lunch / Dinner), Bakery items (snacks),Agree,Agree,Strongly agree,Agree,Agree,Agree,Strongly agree,Agree,Neutral,Neutral,Neutral,Disagree,Disagree,Disagree,Agree,Agree,Strongly agree,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Strongly Agree,Agree,Neutral,Agree,Yes,Important,Very Important,Important,Very Important,Important,Very Important,Very Important,Very Important,Yes,I love ordering online
+22,Female,Single,Student,No Income,Graduate,2,12.9515,77.4921,560056,Web browser, Walk-in,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Neutral,Agree,Agree,Agree,Agree,Disagree,Agree,Disagree,Agree,Neutral,Agree,Disagree,Disagree,Strongly disagree,Disagree,Strongly disagree,Strongly disagree,Maybe,Weekend (Sat & Sun),45 minutes,Agree,Agree,Strongly Agree,Strongly Agree,Strongly Agree,Yes,Moderately Important,Slightly Important,Moderately Important,Important,Important,Very Important,Important,Very Important,Yes,BEST BEST POSITIVE REVIEWS
+30,Male,Married,Employee,More than 50000,Graduate,5,12.9719,77.5128,560072,Food delivery apps, Direct call,Breakfast, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Disagree,Agree,Disagree,Disagree,Agree,Agree,Agree,Agree,Agree,Disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Agree,Agree,Agree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Strongly Agree,Agree,Neutral,Agree,Yes,Important,Important,Very Important,Very Important,Slightly Important,Moderately Important,Important,Very Important,No,Delivery in my place is slow as turtle
+23,Female,Prefer not to say,Employee,10001 to 25000,Graduate,4,12.9048,77.6821,560036,Food delivery apps, Direct call,Lunch, Dinner,Veg foods (Breakfast / Lunch / Dinner), Ice cream / Cool drinks,Agree,Agree,Strongly agree,Agree,Agree,Strongly agree,Strongly agree,Disagree,Neutral,Neutral,Agree,Agree,Agree,Disagree,Agree,Neutral,Agree,Strongly agree,Strongly agree,Strongly agree,Yes,Weekend (Sat & Sun),30 minutes,Agree,Agree,Disagree,Disagree,Disagree,Yes,Important,Important,Very Important,Important,Very Important,Very Important,Very Important,Very Important,Yes,FAST FAST FAST FAST FAST
+19,Male,Single,Student,No Income,Graduate,6,12.9048,77.6821,560036,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Neutral,Neutral,Agree,Agree,Disagree,Agree,Disagree,Agree,Agree,Disagree,Disagree,Strongly disagree,Strongly disagree,Agree,Disagree,Disagree,Agree,Neutral,Disagree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Strongly Agree,Agree,Strongly Agree,Strongly Agree,Yes,Important,Very Important,Important,Important,Moderately Important,Important,Important,Very Important,Yes,"I can forgive deliveries if they are late, but can't compromise with quality"
+21,Female,Single,Student,No Income,Graduate,2,13.0103,77.5796,560080,Food delivery apps, Web browser,Breakfast, Lunch,Veg foods (Breakfast / Lunch / Dinner), Ice cream / Cool drinks,Agree,Strongly agree,Agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Disagree,Agree,Agree,Disagree,Disagree,Strongly disagree,Agree,Agree,Agree,Agree,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),45 minutes,Disagree,Disagree,Disagree,Disagree,Disagree,Yes,Important,Very Important,Important,Important,Slightly Important,Moderately Important,Important,Very Important,Yes,Fast as sonic !!!
+23,Male,Single,Student,No Income,Graduate,4,13.0103,77.5796,560080,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Neutral,Disagree,Disagree,Strongly disagree,Disagree,Agree,Agree,Agree,Agree,Disagree,Agree,Disagree,Agree,Disagree,Disagree,Disagree,Disagree,Strongly disagree,Maybe,Anytime (Mon-Sun),60 minutes,Disagree,Disagree,Disagree,Disagree,Neutral,Maybe,Important,Moderately Important,Slightly Important,Slightly Important,Slightly Important,Moderately Important,Moderately Important,Important,Yes,I find no issues with the online food delivery. But requires a lil bit faster delivery
+24,Female,Single,Employee,25001 to 50000,Graduate,3,12.8893,77.6399,560068,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Agree,Agree,Strongly agree,Strongly agree,Disagree,Disagree,Agree,Disagree,Agree,Strongly disagree,Agree,Agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Yes,Anytime (Mon-Sun),60 minutes,Agree,Agree,Strongly Agree,Agree,Strongly Agree,Yes,Important,Very Important,Very Important,Important,Moderately Important,Important,Important,Important,Yes,I have certain high rated restaurants where I order and they deliver fast 
+26,Female,Married,Employee,10001 to 25000,Post Graduate,2,12.9828,77.6131,560042,Food delivery apps, Direct call,Lunch, Snacks,Veg foods (Breakfast / Lunch / Dinner), Sweets,Disagree,Disagree,Disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Agree,Agree,Strongly agree,Disagree,Disagree,Disagree,Disagree,Agree,Agree,Agree,Agree,Strongly agree,Strongly agree,Yes,Anytime (Mon-Sun),15 minutes,Agree,Agree,Neutral,Agree,Agree,Yes,Important,Important,Very Important,Important,Very Important,Very Important,Very Important,Very Important,No,No reason. I prefer home cooked food. I had bad experiences ordering online
+26,Female,Single,Student,No Income,Ph.D,2,12.9757,77.5586,560023,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Strongly agree,Strongly agree,Agree,Strongly agree,Strongly agree,Disagree,Agree,Agree,Disagree,Disagree,Strongly disagree,Strongly disagree,Disagree,Disagree,Disagree,Strongly disagree,Strongly disagree,Disagree,No,Anytime (Mon-Sun),60 minutes,Agree,Strongly Agree,Agree,Agree,Neutral,Yes,Important,Very Important,Important,Important,Important,Important,Very Important,Important,Yes,TOP RATING TOP RATING RATING FIVE PLUS FIVE PLUS FOUR PLUS FOUR PLUS
+25,Female,Single,Student,10001 to 25000,Graduate,2,12.9757,77.5586,560023,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Agree,Agree,Agree,Agree,Disagree,Disagree,Neutral,Disagree,Disagree,Strongly disagree,Strongly disagree,Agree,Agree,Strongly agree,Agree,Strongly agree,Agree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Agree,Agree,Agree,Yes,Important,Moderately Important,Important,Important,Slightly Important,Moderately Important,Very Important,Very Important,Yes,Favorite service to reduce our hunger
+28,Female,Married,Employee,25001 to 50000,Graduate,5,12.9757,77.5586,560023,Food delivery apps, Walk-in,Lunch, Snacks,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Disagree,Disagree,Strongly disagree,Strongly disagree,Disagree,Disagree,Agree,Agree,Agree,Agree,Disagree,Disagree,Disagree,Agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Yes,Weekend (Sat & Sun),15 minutes,Agree,Strongly Agree,Agree,Disagree,Disagree,Yes,Important,Very Important,Very Important,Very Important,Moderately Important,Important,Very Important,Important,No,BAD BAD BAD POOR POOR POOR RATING
+25,Female,Single,Student,No Income,Post Graduate,3,13.0734,77.5464,560014,Food delivery apps, Walk-in,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Strongly agree,Agree,Strongly agree,Strongly agree,Agree,Agree,Disagree,Disagree,Disagree,Strongly disagree,Strongly disagree,Agree,Agree,Strongly agree,Strongly disagree,Strongly disagree,Strongly disagree,No,Anytime (Mon-Sun),More than 60 minutes,Agree,Strongly Agree,Agree,Agree,Agree,Maybe,Slightly Important,Moderately Important,Moderately Important,Slightly Important,Important,Important,Moderately Important,Moderately Important,Yes,"If Packaging is hygienic,I would rate them high"
+20,Female,Single,Student,No Income,Graduate,2,13.0734,77.5464,560014,Food delivery apps, Walk-in,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Disagree,Disagree,Disagree,Agree,Agree,Agree,Agree,Agree,Disagree,Strongly disagree,Strongly disagree,Neutral,Disagree,Neutral,Strongly disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Strongly Agree,Agree,Strongly disagree,Strongly disagree,Maybe,Moderately Important,Important,Important,Slightly Important,Slightly Important,Very Important,Important,Important,Yes,Delivery people are sweet and fast
+27,Female,Married,Self Employeed,More than 50000,Graduate,6,13.0734,77.5464,560014,Food delivery apps, Direct call,Breakfast, Lunch,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Disagree,Disagree,Strongly disagree,Disagree,Strongly disagree,Strongly disagree,Agree,Agree,Agree,Disagree,Agree,Agree,Disagree,Agree,Agree,Agree,Disagree,Strongly disagree,Strongly disagree,Yes,Weekdays (Mon-Fri),15 minutes,Disagree,Strongly Agree,Disagree,Disagree,Disagree,Yes,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,No,I need quality food arriving at my doorsteps within the stipulated time
+25,Female,Married,Employee,25001 to 50000,Graduate,3,13.0626,77.5284,560015,Food delivery apps, Walk-in,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Disagree,Disagree,Strongly disagree,Disagree,Disagree,Disagree,Disagree,Neutral,Agree,Neutral,Agree,Agree,Strongly disagree,Strongly agree,Strongly agree,Strongly agree,Strongly disagree,Strongly disagree,Strongly disagree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Agree,Strongly Agree,Strongly Agree,Strongly Agree,Yes,Important,Important,Important,Important,Important,Important,Important,Important,No,Can't understand what delivery people are speaking. Language barrier !
+24,Female,Single,Student,No Income,Post Graduate,5,13.0626,77.5284,560015,Food delivery apps, Direct call,Breakfast, Lunch,Veg foods (Breakfast / Lunch / Dinner), Sweets,Disagree,Disagree,Disagree,Strongly disagree,Disagree,Disagree,Strongly disagree,Agree,Agree,Agree,Agree,Agree,Agree,Strongly disagree,Agree,Agree,Agree,Agree,Strongly agree,Strongly agree,Yes,Weekend (Sat & Sun),30 minutes,Agree,Disagree,Disagree,Disagree,Agree,Yes,Important,Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,No,less responsive delivery men. Also prevalent late deliveries
+25,Female,Married,Employee,More than 50000,Graduate,1,12.977,77.5773,560009,Food delivery apps, Direct call,Lunch, Snacks,Veg foods (Breakfast / Lunch / Dinner), Sweets,Disagree,Disagree,Disagree,Disagree,Neutral,Disagree,Disagree,Agree,Agree,Agree,Strongly disagree,Disagree,Neutral,Disagree,Disagree,Neutral,Agree,Strongly disagree,Strongly disagree,Strongly disagree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Agree,Agree,Disagree,Neutral,Yes,Important,Important,Very Important,Very Important,Important,Important,Very Important,Very Important,No,poor delivery men really rushing fast to deliver the food. But there are many spillage issues as well
+25,Female,Prefer not to say,Employee,25001 to 50000,Post Graduate,3,12.998,77.6227,560005,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Disagree,Neutral,Strongly disagree,Disagree,Neutral,Disagree,Strongly disagree,Agree,Agree,Neutral,Disagree,Disagree,Disagree,Disagree,Neutral,Neutral,Disagree,Disagree,Disagree,Yes,Weekend (Sat & Sun),45 minutes,Agree,Agree,Strongly Agree,Agree,Agree,Yes,Important,Important,Important,Important,Very Important,Very Important,Very Important,Very Important,No,Had many bad experiences with respect to delivery
+24,Female,Married,Student,Below Rs.10000,Ph.D,5,13.0626,77.5284,560015,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Strongly agree,Agree,Strongly agree,Strongly agree,Disagree,Disagree,Agree,Disagree,Strongly disagree,Strongly disagree,Disagree,Disagree,Disagree,Disagree,Strongly disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),45 minutes,Disagree,Disagree,Disagree,Strongly disagree,Neutral,Yes,Slightly Important,Moderately Important,Important,Important,Important,Moderately Important,Important,Important,Yes,"LOVE THE PRICE AND OFFER OFFERED BY SWIGGY, USUALLY RATE FOUR PLUS"
+24,Male,Single,Student,No Income,Post Graduate,3,13.0626,77.5284,560015,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Strongly agree,Agree,Agree,Agree,Disagree,Strongly disagree,Disagree,Strongly disagree,Disagree,Neutral,Neutral,Agree,Agree,Disagree,Disagree,Disagree,Disagree,Maybe,Weekend (Sat & Sun),45 minutes,Disagree,Neutral,Agree,Disagree,Disagree,Yes,Very Important,Important,Moderately Important,Important,Moderately Important,Important,Important,Very Important,Yes,Good price and offers with fast delivery
+22,Male,Single,Employee,10001 to 25000,Graduate,1,13.0138,77.5877,560006,Food delivery apps, Direct call,Lunch, Dinner,Veg foods (Breakfast / Lunch / Dinner), Sweets,Agree,Agree,Strongly agree,Agree,Agree,Strongly agree,Strongly agree,Disagree,Neutral,Agree,Neutral,Disagree,Disagree,Disagree,Agree,Strongly agree,Agree,Strongly disagree,Strongly disagree,Strongly disagree,Yes,Anytime (Mon-Sun),45 minutes,Disagree,Disagree,Disagree,Disagree,Disagree,No,Slightly Important,Moderately Important,Slightly Important,Unimportant,Unimportant,Unimportant,Slightly Important,Slightly Important,Yes,NIL
+28,Male,Single,Employee,25001 to 50000,Post Graduate,2,13.0138,77.5877,560006,Food delivery apps, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Disagree,Strongly disagree,Disagree,Disagree,Strongly disagree,Disagree,Agree,Agree,Agree,Agree,Agree,Strongly agree,Strongly disagree,Agree,Agree,Agree,Agree,Agree,Strongly disagree,Yes,Anytime (Mon-Sun),30 minutes,Strongly disagree,Agree,Disagree,Disagree,Disagree,Yes,Important,Moderately Important,Important,Moderately Important,Important,Very Important,Important,Very Important,No,Slow delivery and I avoid ordering from poor rated restaurant
+25,Female,Single,Employee,More than 50000,Post Graduate,6,13.014,77.5658,560012,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly disagree,Disagree,Disagree,Disagree,Strongly disagree,Agree,Agree,Agree,Agree,Agree,Neutral,Agree,Agree,Disagree,Disagree,Neutral,Neutral,Agree,Disagree,Disagree,Yes,Weekdays (Mon-Fri),15 minutes,Disagree,Disagree,Neutral,Agree,Agree,Yes,Important,Important,Very Important,Very Important,Important,Very Important,Very Important,Very Important,No,I'd rather cook at home during covid-19
+22,Male,Single,Student,Below Rs.10000,Post Graduate,3,12.9551,77.6593,560017,Food delivery apps, Direct call,Lunch, Snacks,Non Veg foods (Lunch / Dinner), Ice cream / Cool drinks,Strongly agree,Strongly agree,Strongly agree,Neutral,Neutral,Disagree,Neutral,Disagree,Neutral,Neutral,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Strongly agree,Agree,Neutral,Yes,Anytime (Mon-Sun),45 minutes,Agree,Strongly Agree,Neutral,Neutral,Agree,Yes,Important,Very Important,Moderately Important,Very Important,Very Important,Important,Very Important,Moderately Important,Yes,"Many a times payment gateways are an issue, so getting the refund and the surcharges are an inconvenience"
+22,Female,Single,Student,No Income,Graduate,6,12.9473,77.5616,560019,Food delivery apps, Walk-in,Snacks, Dinner,Veg foods (Breakfast / Lunch / Dinner), Bakery items (snacks),Agree,Agree,Strongly agree,Agree,Strongly agree,Agree,Agree,Agree,Strongly agree,Neutral,Agree,Disagree,Disagree,Neutral,Agree,Agree,Agree,Disagree,Disagree,Neutral,Yes,Anytime (Mon-Sun),30 minutes,Disagree,Agree,Agree,Neutral,Agree,Yes,Very Important,Important,Moderately Important,Very Important,Very Important,Very Important,Very Important,Important,Yes,nil
+22,Male,Single,Student,Below Rs.10000,Post Graduate,4,12.985,77.5533,560010,Walk-in, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Agree,Agree,Neutral,Neutral,Agree,Strongly agree,Strongly agree,Agree,Strongly agree,Agree,Disagree,Strongly agree,Strongly agree,Neutral,Neutral,Neutral,Disagree,Yes,Weekend (Sat & Sun),30 minutes,Agree,Agree,Agree,Agree,Agree,Yes,Important,Important,Moderately Important,Important,Important,Important,Very Important,Very Important,Yes,NIL
+27,Female,Married,Employee,More than 50000,Post Graduate,2,12.9299,77.6848,560103,Food delivery apps, Walk-in,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Neutral,Agree,Agree,Neutral,Agree,Disagree,Neutral,Agree,Agree,Agree,Agree,Agree,Strongly agree,Strongly agree,Strongly agree,Agree,Strongly agree,Strongly agree,Yes,Anytime (Mon-Sun),45 minutes,Neutral,Agree,Neutral,Neutral,Neutral,Yes,Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Yes,NIL
+22,Male,Single,Student,No Income,Graduate,3,12.977,77.5773,560009,Food delivery apps, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Strongly agree,Agree,Strongly agree,Agree,Neutral,Agree,Disagree,Agree,Disagree,Agree,Disagree,Strongly agree,Strongly disagree,Neutral,Disagree,Disagree,Strongly agree,Strongly agree,Strongly agree,No,Anytime (Mon-Sun),45 minutes,Agree,Strongly Agree,Disagree,Agree,Strongly Agree,Yes,Very Important,Very Important,Unimportant,Important,Very Important,Very Important,Very Important,Very Important,Yes,Nil
+24,Female,Single,Student,No Income,Post Graduate,3,12.9828,77.6131,560042,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Ice cream / Cool drinks,Strongly agree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Neutral,Neutral,Neutral,Neutral,Agree,Agree,Agree,Neutral,Agree,Agree,Yes,Weekend (Sat & Sun),45 minutes,Agree,Agree,Agree,Neutral,Disagree,Maybe,Moderately Important,Important,Moderately Important,Slightly Important,Important,Moderately Important,Very Important,Very Important,Yes,NIL
+23,Female,Single,Student,No Income,Post Graduate,2,12.9766,77.5993,560001,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Agree,Agree,Neutral,Agree,Agree,Agree,Neutral,Agree,Neutral,Neutral,Disagree,Agree,Agree,Agree,Agree,Agree,Strongly agree,Yes,Anytime (Mon-Sun),30 minutes,Disagree,Disagree,Neutral,Agree,Agree,Maybe,Important,Important,Important,Important,Important,Important,Important,Important,Yes,Nil
+23,Female,Single,Student,No Income,Post Graduate,4,12.9854,77.7081,560048,Food delivery apps, Walk-in,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Agree,Agree,Neutral,Neutral,Neutral,Neutral,Neutral,Agree,Agree,Agree,Neutral,Agree,Agree,Yes,Weekend (Sat & Sun),45 minutes,Agree,Agree,Agree,Disagree,Disagree,Yes,Moderately Important,Very Important,Moderately Important,Moderately Important,Moderately Important,Moderately Important,Very Important,Very Important,Yes,Nil
+22,Female,Single,Student,No Income,Post Graduate,5,12.985,77.5533,560010,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Agree,Agree,Neutral,Agree,Agree,Strongly agree,Agree,Strongly agree,Agree,Agree,Agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Agree,Strongly Agree,Neutral,Agree,Yes,Important,Very Important,Important,Important,Very Important,Very Important,Very Important,Very Important,Yes,Nil
+23,Male,Single,Student,Below Rs.10000,Post Graduate,2,12.977,77.5773,560009,Food delivery apps, Walk-in,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Strongly agree,Strongly agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Strongly agree,Disagree,Agree,Strongly agree,Strongly agree,Neutral,Strongly agree,Strongly agree,Agree,Strongly agree,Strongly agree,Disagree,Maybe,Weekend (Sat & Sun),45 minutes,Strongly Agree,Disagree,Agree,Disagree,Strongly Agree,Yes,Important,Very Important,Important,Very Important,Very Important,Important,Very Important,Very Important,Yes,Language barrier is also one major issue. Moslty these delivery boys are only familiar with Kanada which creates a problem while addressing any issue.
+32,Male,Married,Employee,More than 50000,Graduate,5,12.9037,77.5376,560061,Web browser, Walk-in,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Neutral,Neutral,Agree,Agree,Agree,Disagree,Disagree,Agree,Neutral,Neutral,Neutral,Disagree,Disagree,Neutral,Neutral,Disagree,Neutral,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Neutral,Neutral,Agree,Neutral,No,Important,Moderately Important,Slightly Important,Slightly Important,Slightly Important,Moderately Important,Moderately Important,Moderately Important,Yes,Smooth delivery service 
+25,Female,Married,Student,No Income,Post Graduate,2,12.8834,77.5486,560062,Food delivery apps, Web browser,Snacks, Dinner,Sweets, Ice cream / Cool drinks,Agree,Disagree,Agree,Neutral,Neutral,Agree,Agree,Disagree,Neutral,Agree,Disagree,Disagree,Strongly disagree,Strongly disagree,Strongly agree,Strongly agree,Strongly agree,Disagree,Agree,Agree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Neutral,Agree,Neutral,Neutral,Yes,Important,Moderately Important,Important,Moderately Important,Slightly Important,Moderately Important,Important,Very Important,Yes,Fast delivery is required. But quality is super good
+28,Male,Single,Self Employeed,10001 to 25000,Post Graduate,2,12.9149,77.5635,560070,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Neutral,Agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Agree,Disagree,Agree,Disagree,Disagree,Strongly disagree,Strongly disagree,Disagree,Disagree,Disagree,Strongly disagree,Strongly disagree,Strongly disagree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Neutral,Neutral,Agree,Neutral,Maybe,Moderately Important,Slightly Important,Slightly Important,Moderately Important,Slightly Important,Important,Very Important,Very Important,Yes,LOVE SWIGGY AND ZOMATO. Literally the best
+21,Female,Single,Student,No Income,Post Graduate,3,12.9149,77.5635,560070,Food delivery apps, Walk-in,Breakfast, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Neutral,Agree,Neutral,Agree,Neutral,Neutral,Agree,Agree,Disagree,Disagree,Disagree,Disagree,Disagree,Agree,Disagree,Neutral,Neutral,Disagree,Neutral,Maybe,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Agree,Neutral,Agree,Yes,Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Yes,The delivery to my locations are done pretty fast
+24,Male,Single,Student,No Income,Post Graduate,2,12.9706,77.6529,560075,Food delivery apps, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Agree,Neutral,Agree,Strongly agree,Disagree,Disagree,Agree,Disagree,Disagree,Neutral,Disagree,Agree,Neutral,Neutral,Disagree,Disagree,Strongly disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Neutral,Agree,Neutral,Disagree,Yes,Important,Slightly Important,Important,Moderately Important,Moderately Important,Important,Important,Very Important,Yes,NIL
+26,Female,Married,Self Employeed,10001 to 25000,Graduate,5,12.9706,77.6529,560075,Food delivery apps, Direct call,Dinner, Dinner,Veg foods (Breakfast / Lunch / Dinner), Sweets,Disagree,Disagree,Neutral,Agree,Disagree,Neutral,Agree,Agree,Agree,Agree,Neutral,Neutral,Agree,Agree,Disagree,Neutral,Neutral,Disagree,Disagree,Disagree,Maybe,Anytime (Mon-Sun),45 minutes,Agree,Agree,Neutral,Disagree,Disagree,Maybe,Important,Important,Very Important,Important,Important,Important,Important,Important,No,I require the food to be undamaged. Mostly the food is tampered
+32,Male,Married,Employee,25001 to 50000,Graduate,3,12.9706,77.6529,560075,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Strongly agree,Agree,Neutral,Disagree,Disagree,Disagree,Neutral,Agree,Agree,Neutral,Disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Neutral,Agree,Agree,Strongly Agree,Yes,Important,Moderately Important,Important,Moderately Important,Important,Important,Important,Important,Yes,Best Best Best Best !!!!
+29,Male,Single,Self Employeed,More than 50000,Graduate,6,12.8845,77.6036,560076,Food delivery apps, Web browser,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Neutral,Disagree,Agree,Agree,Agree,Disagree,Disagree,Disagree,Neutral,Disagree,Disagree,Strongly disagree,Agree,Neutral,Agree,Disagree,Disagree,Disagree,Yes,Weekend (Sat & Sun),More than 60 minutes,Agree,Neutral,Agree,Neutral,Agree,No,Moderately Important,Slightly Important,Moderately Important,Slightly Important,Slightly Important,Moderately Important,Moderately Important,Slightly Important,Yes,Love to order online 
+21,Male,Single,Student,No Income,Graduate,5,12.9783,77.6408,560038,Food delivery apps, Web browser,Lunch, Snacks,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Neutral,Agree,Neutral,Agree,Agree,Disagree,Disagree,Disagree,Disagree,Neutral,Disagree,Agree,Agree,Strongly agree,Yes,Weekend (Sat & Sun),45 minutes,Agree,Neutral,Agree,Strongly Agree,Strongly Agree,Yes,Important,Important,Very Important,Important,Slightly Important,Moderately Important,Important,Important,Yes,I love ordering for my family from swiggy
+24,Female,Single,Self Employeed,25001 to 50000,Post Graduate,3,13.0103,77.5796,560080,Food delivery apps, Direct call,Dinner, Dinner,Veg foods (Breakfast / Lunch / Dinner), Sweets,Disagree,Disagree,Disagree,Neutral,Agree,Agree,Agree,Agree,Disagree,Disagree,Disagree,Disagree,Neutral,Agree,Agree,Neutral,Agree,Disagree,Disagree,Strongly disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Neutral,Neutral,Agree,Neutral,Maybe,Important,Important,Important,Moderately Important,Moderately Important,Important,Important,Important,No,I need faster delivery.
+26,Male,Prefer not to say,Self Employeed,More than 50000,Post Graduate,2,13.0103,77.5796,560080,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Strongly agree,Neutral,Strongly agree,Agree,Agree,Disagree,Disagree,Disagree,Disagree,Disagree,Agree,Disagree,Disagree,Agree,Agree,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Agree,Strongly Agree,Agree,Agree,No,Moderately Important,Moderately Important,Slightly Important,Slightly Important,Slightly Important,Slightly Important,Moderately Important,Moderately Important,Yes,Crazily fast delivery people besides having high traffic
+21,Male,Married,Employee,More than 50000,Ph.D,3,12.9306,77.5434,560085,Food delivery apps, Web browser,Lunch, Snacks,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Neutral,Agree,Agree,Neutral,Agree,Agree,Neutral,Disagree,Disagree,Disagree,Neutral,Disagree,Disagree,Disagree,Disagree,Disagree,Neutral,Disagree,Disagree,No,Anytime (Mon-Sun),30 minutes,Agree,Neutral,Agree,Neutral,Disagree,Yes,Important,Important,Very Important,Important,Moderately Important,Important,Important,Moderately Important,Yes,BEST BEST BEST !! WONDERFUL WONDERFUL !! FAST FAST FAST !!
+29,Male,Single,Employee,25001 to 50000,Graduate,3,13.0641,77.5931,560092,Food delivery apps, Web browser,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Disagree,Disagree,Strongly disagree,Disagree,Disagree,Strongly disagree,Agree,Neutral,Agree,Neutral,Agree,Neutral,Agree,Neutral,Agree,Agree,Strongly disagree,Strongly disagree,Strongly disagree,Yes,Anytime (Mon-Sun),45 minutes,Neutral,Agree,Agree,Agree,Strongly Agree,Yes,Important,Very Important,Important,Very Important,Moderately Important,Important,Important,Very Important,No,Need quality food delivery. had worst experience of spilled food
+22,Male,Single,Student,No Income,Graduate,3,13.0158,77.539,560096,Food delivery apps, Web browser,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Neutral,Neutral,Agree,Strongly agree,Agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Strongly agree,Disagree,Disagree,Neutral,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Agree,Neutral,Agree,Yes,Important,Very Important,Important,Very Important,Important,Very Important,Very Important,Important,Yes,NIL
+24,Male,Single,Student,No Income,Graduate,2,12.9561,77.5921,560027,Direct call, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Strongly agree,Neutral,Neutral,Neutral,Neutral,Neutral,Strongly disagree,Disagree,Neutral,Disagree,Disagree,Neutral,Disagree,Agree,Agree,Agree,Agree,Strongly agree,Neutral,Yes,Weekend (Sat & Sun),30 minutes,Agree,Agree,Agree,Neutral,Neutral,Yes,Important,Important,Moderately Important,Important,Very Important,Very Important,Very Important,Very Important,Yes,NIL
+27,Male,Married,Employee,25001 to 50000,Graduate,2,12.8845,77.6036,560076,Food delivery apps, Web browser,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Disagree,Agree,Neutral,Strongly agree,Neutral,Agree,Disagree,Disagree,Agree,Disagree,Disagree,Disagree,Neutral,Agree,Neutral,Agree,Strongly agree,Agree,Neutral,No,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Agree,Agree,Strongly Agree,Yes,Important,Moderately Important,Important,Moderately Important,Moderately Important,Important,Moderately Important,Moderately Important,Yes,I would avoid restaurants which has less rating 
+23,Female,Single,Student,No Income,Post Graduate,3,12.9369,77.6407,560095,Food delivery apps, Web browser,Snacks, Dinner,Veg foods (Breakfast / Lunch / Dinner), Sweets,Disagree,Neutral,Neutral,Disagree,Disagree,Neutral,Strongly disagree,Agree,Agree,Agree,Agree,Neutral,Disagree,Disagree,Disagree,Neutral,Agree,Disagree,Strongly disagree,Strongly disagree,Yes,Anytime (Mon-Sun),15 minutes,Agree,Agree,Strongly Agree,Agree,Strongly Agree,Maybe,Important,Important,Very Important,Important,Moderately Important,Moderately Important,Important,Important,No,Faster delivery is needed !!
+32,Male,Married,Employee,More than 50000,Post Graduate,6,12.9369,77.6407,560095,Food delivery apps, Walk-in,Lunch, Snacks,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Disagree,Neutral,Neutral,Agree,Neutral,Agree,Agree,Disagree,Neutral,Disagree,Disagree,Disagree,Agree,Neutral,Agree,Disagree,Disagree,Disagree,Yes,Weekend (Sat & Sun),45 minutes,Agree,Neutral,Agree,Agree,Strongly Agree,Yes,Important,Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Yes,My entire family loves swiggy
+22,Female,Single,Student,No Income,Graduate,2,12.9369,77.6407,560095,Food delivery apps, Direct call,Breakfast, Snacks,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Disagree,Disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,No,Anytime (Mon-Sun),More than 60 minutes,Agree,Agree,Strongly Agree,Agree,Disagree,Maybe,Important,Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Yes,Delivery people are working hard. Can't complain
+22,Female,Single,Employee,25001 to 50000,Graduate,4,12.8845,77.6036,560076,Food delivery apps, Walk-in,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Agree,Neutral,Neutral,Agree,Neutral,Agree,Agree,Strongly agree,Neutral,Strongly agree,Agree,Agree,Disagree,Disagree,Disagree,Disagree,Agree,Agree,Agree,Yes,Anytime (Mon-Sun),45 minutes,Strongly Agree,Strongly Agree,Strongly Agree,Agree,Agree,Yes,Important,Very Important,Very Important,Very Important,Very Important,Important,Very Important,Important,Yes,NIL
+26,Male,Married,Employee,More than 50000,Graduate,4,12.9048,77.6821,560036,Food delivery apps, Web browser,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Strongly agree,Disagree,Disagree,Strongly disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Agree,Disagree,Disagree,Strongly disagree,No,Anytime (Mon-Sun),45 minutes,Agree,Strongly Agree,Agree,Agree,Strongly Agree,Yes,Important,Important,Important,Important,Slightly Important,Moderately Important,Important,Important,Yes,"My home is near Phoenix marketcity, so time taken for delivery is less"
+25,Female,Single,Student,No Income,Ph.D,3,12.9048,77.6821,560036,Food delivery apps, Web browser,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Agree,Strongly agree,Agree,Strongly agree,Disagree,Disagree,Strongly disagree,Disagree,Strongly disagree,Strongly disagree,Strongly disagree,Disagree,Disagree,Agree,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Strongly Agree,Agree,Neutral,Agree,No,Slightly Important,Moderately Important,Slightly Important,Slightly Important,Important,Important,Important,Important,Yes,I'm quite satisfied with the service
+20,Male,Single,Student,No Income,Graduate,2,12.9261,77.6221,560034,Food delivery apps, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Strongly agree,Disagree,Disagree,Agree,Disagree,Disagree,Strongly disagree,Strongly disagree,Agree,Strongly agree,Agree,Strongly disagree,Strongly disagree,Strongly disagree,Yes,Weekend (Sat & Sun),45 minutes,Agree,Strongly Agree,Agree,Agree,Disagree,Yes,Important,Moderately Important,Moderately Important,Important,Important,Very Important,Very Important,Important,Yes,I had no issues as per my experience. But heard delivery people are sometimes rude
+29,Male,Married,Employee,25001 to 50000,Graduate,4,12.9261,77.6221,560034,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Agree,Agree,Disagree,Strongly disagree,Disagree,Strongly agree,Agree,Strongly agree,Disagree,Neutral,Agree,Strongly disagree,Strongly disagree,Disagree,Agree,Agree,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),15 minutes,Agree,Agree,Strongly Agree,Agree,Agree,Maybe,Important,Moderately Important,Important,Important,Very Important,Very Important,Very Important,Very Important,No,I had few health issues after consuming delivered food. Restaurants need to improve their quality and delivery people should delivery it without tampering which might lead to contamination of food
+23,Female,Single,Student,No Income,Graduate,1,12.977,77.5773,560009,Food delivery apps, Direct call,Breakfast, Lunch,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly disagree,Neutral,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Disagree,Disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Disagree,Strongly disagree,Strongly disagree,Strongly disagree,Yes,Anytime (Mon-Sun),15 minutes,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Disagree,No,Slightly Important,Unimportant,Unimportant,Unimportant,Unimportant,Slightly Important,Unimportant,Unimportant,Yes,NIL
+25,Male,Single,Self Employeed,More than 50000,Graduate,2,12.9783,77.6408,560038,Food delivery apps, Walk-in,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Agree,Agree,Neutral,Disagree,Disagree,Strongly disagree,Disagree,Disagree,Neutral,Agree,Disagree,Strongly disagree,Strongly disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Agree,Agree,Agree,Yes,Important,Important,Important,Very Important,Slightly Important,Moderately Important,Important,Very Important,Yes,I order most of my meals on swiggy or zomato depending upon the offers
+29,Female,Married,Employee,25001 to 50000,Graduate,4,12.9783,77.6408,560038,Food delivery apps, Walk-in,Lunch, Dinner,Veg foods (Breakfast / Lunch / Dinner), Sweets,Disagree,Neutral,Disagree,Neutral,Strongly disagree,Agree,Agree,Agree,Agree,Agree,Agree,Disagree,Disagree,Disagree,Disagree,Agree,Agree,Strongly disagree,Neutral,Agree,Yes,Anytime (Mon-Sun),15 minutes,Agree,Agree,Disagree,Agree,Neutral,Yes,Slightly Important,Moderately Important,Moderately Important,Very Important,Slightly Important,Moderately Important,Important,Important,No,I need the order to be delivered soon. It takes a lot of time 
+27,Male,Married,Self Employeed,25001 to 50000,Graduate,6,12.9217,77.5936,560041,Food delivery apps, Walk-in,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Disagree,Agree,Neutral,Agree,Agree,Strongly agree,Agree,Agree,Agree,Strongly agree,Agree,Disagree,Agree,Agree,Agree,Agree,Disagree,Disagree,Disagree,No,Anytime (Mon-Sun),60 minutes,Agree,Agree,Agree,Agree,Strongly Agree,Yes,Important,Important,Very Important,Very Important,Moderately Important,Important,Important,Important,No,Food needs to have good quality. It leads me to give higher rating
+25,Male,Single,Self Employeed,10001 to 25000,Graduate,3,13.0206,77.6479,560043,Food delivery apps, Walk-in,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Agree,Agree,Agree,Agree,Disagree,Disagree,Strongly disagree,Disagree,Disagree,Disagree,Disagree,Agree,Neutral,Agree,Neutral,Disagree,Disagree,No,Anytime (Mon-Sun),60 minutes,Agree,Strongly Agree,Agree,Agree,Agree,No,Moderately Important,Moderately Important,Important,Important,Slightly Important,Moderately Important,Important,Important,Yes,I appreciate the efforts of hardworking delivery people
+21,Male,Single,Student,No Income,Graduate,2,13.0012,77.5995,560046,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Disagree,Neutral,Disagree,Neutral,Disagree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Disagree,Neutral,Agree,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Agree,Agree,Strongly Agree,Yes,Moderately Important,Important,Important,Important,Important,Very Important,Very Important,Very Important,No,Delivery is late. Need quick updates on location of order
+23,Male,Single,Student,No Income,Graduate,3,13.0223,77.7132,560049,Food delivery apps, Web browser,Breakfast, Dinner,Veg foods (Breakfast / Lunch / Dinner), Sweets,Agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Strongly agree,Disagree,Disagree,Disagree,Neutral,Agree,Disagree,Disagree,Disagree,Neutral,Disagree,Agree,Disagree,Neutral,Yes,Anytime (Mon-Sun),30 minutes,Agree,Neutral,Agree,Agree,Neutral,Yes,Important,Important,Very Important,Important,Slightly Important,Moderately Important,Important,Very Important,Yes,I have no trouble with delivery
+24,Female,Single,Employee,10001 to 25000,Post Graduate,4,12.9337,77.59,560011,Food delivery apps, Direct call,Snacks, Dinner,Veg foods (Breakfast / Lunch / Dinner), Ice cream / Cool drinks,Agree,Agree,Agree,Neutral,Disagree,Neutral,Neutral,Agree,Agree,Neutral,Neutral,Neutral,Disagree,Neutral,Neutral,Agree,Neutral,Neutral,Neutral,Neutral,Yes,Anytime (Mon-Sun),45 minutes,Neutral,Neutral,Agree,Agree,Agree,Yes,Important,Moderately Important,Moderately Important,Important,Important,Important,Important,Important,Yes,Nil
+22,Male,Married,Self Employeed,10001 to 25000,School,3,12.982,77.6256,560008,Food delivery apps, Walk-in,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Strongly agree,Neutral,Neutral,Strongly agree,Disagree,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Yes,Weekend (Sat & Sun),45 minutes,Neutral,Neutral,Neutral,Disagree,Neutral,No,Unimportant,Slightly Important,Unimportant,Unimportant,Very Important,Very Important,Very Important,Very Important,Yes,Very few times I have  had bad experienced by aggregator or restaurant 
+28,Male,Married,Employee,25001 to 50000,Post Graduate,5,13.0262,77.62,560045,Food delivery apps, Walk-in,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Agree,Agree,Neutral,Neutral,Disagree,Disagree,Agree,Disagree,Agree,Agree,Neutral,Disagree,Agree,Agree,Neutral,Disagree,Neutral,Yes,Anytime (Mon-Sun),45 minutes,Agree,Agree,Disagree,Disagree,Neutral,No,Moderately Important,Slightly Important,Moderately Important,Important,Moderately Important,Moderately Important,Important,Important,Yes,Best service comes with great offers and wonderful service
+26,Male,Single,Employee,10001 to 25000,Graduate,2,12.9217,77.5936,560041,Food delivery apps, Walk-in,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Neutral,Neutral,Agree,Strongly agree,Disagree,Disagree,Neutral,Neutral,Disagree,Neutral,Agree,Agree,Neutral,Agree,Neutral,Neutral,Agree,Yes,Weekend (Sat & Sun),30 minutes,Agree,Agree,Strongly Agree,Neutral,Agree,Yes,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,No,Bad delivery system and less restaurant choices
+22,Female,Single,Employee,25001 to 50000,Graduate,4,12.8845,77.6036,560076,Food delivery apps, Walk-in,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Agree,Neutral,Neutral,Agree,Neutral,Agree,Agree,Strongly agree,Neutral,Strongly agree,Agree,Agree,Disagree,Disagree,Disagree,Disagree,Agree,Agree,Agree,Yes,Anytime (Mon-Sun),45 minutes,Strongly Agree,Strongly Agree,Strongly Agree,Agree,Agree,Yes,Important,Very Important,Very Important,Very Important,Very Important,Important,Very Important,Important,Yes,NIL
+26,Male,Married,Employee,More than 50000,Graduate,4,12.9048,77.6821,560036,Food delivery apps, Web browser,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Strongly agree,Disagree,Disagree,Strongly disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Agree,Disagree,Disagree,Strongly disagree,No,Anytime (Mon-Sun),45 minutes,Agree,Strongly Agree,Agree,Agree,Strongly Agree,Yes,Important,Important,Important,Important,Slightly Important,Moderately Important,Important,Important,Yes,"My home is near Phoenix marketcity, so time taken for delivery is less"
+25,Female,Single,Student,No Income,Ph.D,3,12.9048,77.6821,560036,Food delivery apps, Web browser,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Agree,Strongly agree,Agree,Strongly agree,Disagree,Disagree,Strongly disagree,Disagree,Strongly disagree,Strongly disagree,Strongly disagree,Disagree,Disagree,Agree,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Strongly Agree,Agree,Neutral,Agree,No,Slightly Important,Moderately Important,Slightly Important,Slightly Important,Important,Important,Important,Important,Yes,I'm quite satisfied with the service
+20,Male,Single,Student,No Income,Graduate,2,12.9261,77.6221,560034,Food delivery apps, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Strongly agree,Disagree,Disagree,Agree,Disagree,Disagree,Strongly disagree,Strongly disagree,Agree,Strongly agree,Agree,Strongly disagree,Strongly disagree,Strongly disagree,Yes,Weekend (Sat & Sun),45 minutes,Agree,Strongly Agree,Agree,Agree,Disagree,Yes,Important,Moderately Important,Moderately Important,Important,Important,Very Important,Very Important,Important,Yes,I had no issues as per my experience. But heard delivery people are sometimes rude
+29,Male,Married,Employee,25001 to 50000,Graduate,4,12.9261,77.6221,560034,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Agree,Agree,Disagree,Strongly disagree,Disagree,Strongly agree,Agree,Strongly agree,Disagree,Neutral,Agree,Strongly disagree,Strongly disagree,Disagree,Agree,Agree,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),15 minutes,Agree,Agree,Strongly Agree,Agree,Agree,Maybe,Important,Moderately Important,Important,Important,Very Important,Very Important,Very Important,Very Important,No,I had few health issues after consuming delivered food. Restaurants need to improve their quality and delivery people should delivery it without tampering which might lead to contamination of food
+23,Female,Single,Student,No Income,Graduate,1,12.977,77.5773,560009,Food delivery apps, Direct call,Breakfast, Lunch,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly disagree,Neutral,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Disagree,Disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Disagree,Strongly disagree,Strongly disagree,Strongly disagree,Yes,Anytime (Mon-Sun),15 minutes,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Disagree,No,Slightly Important,Unimportant,Unimportant,Unimportant,Unimportant,Slightly Important,Unimportant,Unimportant,Yes,NIL
+25,Male,Single,Self Employeed,More than 50000,Graduate,2,12.9783,77.6408,560038,Food delivery apps, Walk-in,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Agree,Agree,Neutral,Disagree,Disagree,Strongly disagree,Disagree,Disagree,Neutral,Agree,Disagree,Strongly disagree,Strongly disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Agree,Agree,Agree,Yes,Important,Important,Important,Very Important,Slightly Important,Moderately Important,Important,Very Important,Yes,I order most of my meals on swiggy or zomato depending upon the offers
+29,Female,Married,Employee,25001 to 50000,Graduate,4,12.9783,77.6408,560038,Food delivery apps, Walk-in,Lunch, Dinner,Veg foods (Breakfast / Lunch / Dinner), Sweets,Disagree,Neutral,Disagree,Neutral,Strongly disagree,Agree,Agree,Agree,Agree,Agree,Agree,Disagree,Disagree,Disagree,Disagree,Agree,Agree,Strongly disagree,Neutral,Agree,Yes,Anytime (Mon-Sun),15 minutes,Agree,Agree,Disagree,Agree,Neutral,Yes,Slightly Important,Moderately Important,Moderately Important,Very Important,Slightly Important,Moderately Important,Important,Important,No,I need the order to be delivered soon. It takes a lot of time 
+27,Male,Married,Self Employeed,25001 to 50000,Graduate,6,12.9217,77.5936,560041,Food delivery apps, Walk-in,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Disagree,Agree,Neutral,Agree,Agree,Strongly agree,Agree,Agree,Agree,Strongly agree,Agree,Disagree,Agree,Agree,Agree,Agree,Disagree,Disagree,Disagree,No,Anytime (Mon-Sun),60 minutes,Agree,Agree,Agree,Agree,Strongly Agree,Yes,Important,Important,Very Important,Very Important,Moderately Important,Important,Important,Important,No,Food needs to have good quality. It leads me to give higher rating
+25,Male,Single,Self Employeed,10001 to 25000,Graduate,3,13.0206,77.6479,560043,Food delivery apps, Walk-in,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Agree,Agree,Agree,Agree,Disagree,Disagree,Strongly disagree,Disagree,Disagree,Disagree,Disagree,Agree,Neutral,Agree,Neutral,Disagree,Disagree,No,Anytime (Mon-Sun),60 minutes,Agree,Strongly Agree,Agree,Agree,Agree,No,Moderately Important,Moderately Important,Important,Important,Slightly Important,Moderately Important,Important,Important,Yes,I appreciate the efforts of hardworking delivery people
+21,Male,Single,Student,No Income,Graduate,2,13.0012,77.5995,560046,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Disagree,Neutral,Disagree,Neutral,Disagree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Disagree,Neutral,Agree,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Agree,Agree,Strongly Agree,Yes,Moderately Important,Important,Important,Important,Important,Very Important,Very Important,Very Important,No,Delivery is late. Need quick updates on location of order
+24,Male,Single,Student,No Income,Post Graduate,2,12.9706,77.6529,560075,Food delivery apps, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Agree,Neutral,Agree,Strongly agree,Disagree,Disagree,Agree,Disagree,Disagree,Neutral,Disagree,Agree,Neutral,Neutral,Disagree,Disagree,Strongly disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Neutral,Agree,Neutral,Disagree,Yes,Important,Slightly Important,Important,Moderately Important,Moderately Important,Important,Important,Very Important,Yes,NIL
+26,Female,Married,Self Employeed,10001 to 25000,Graduate,5,12.9706,77.6529,560075,Food delivery apps, Direct call,Dinner, Dinner,Veg foods (Breakfast / Lunch / Dinner), Sweets,Disagree,Disagree,Neutral,Agree,Disagree,Neutral,Agree,Agree,Agree,Agree,Neutral,Neutral,Agree,Agree,Disagree,Neutral,Neutral,Disagree,Disagree,Disagree,Maybe,Anytime (Mon-Sun),45 minutes,Agree,Agree,Neutral,Disagree,Disagree,Maybe,Important,Important,Very Important,Important,Important,Important,Important,Important,No,I require the food to be undamaged. Mostly the food is tampered
+32,Male,Married,Employee,25001 to 50000,Graduate,3,12.9706,77.6529,560075,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Strongly agree,Agree,Neutral,Disagree,Disagree,Disagree,Neutral,Agree,Agree,Neutral,Disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Neutral,Agree,Agree,Strongly Agree,Yes,Important,Moderately Important,Important,Moderately Important,Important,Important,Important,Important,Yes,Best Best Best Best !!!!
+29,Male,Single,Self Employeed,More than 50000,Graduate,6,12.8845,77.6036,560076,Food delivery apps, Web browser,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Neutral,Disagree,Agree,Agree,Agree,Disagree,Disagree,Disagree,Neutral,Disagree,Disagree,Strongly disagree,Agree,Neutral,Agree,Disagree,Disagree,Disagree,Yes,Weekend (Sat & Sun),More than 60 minutes,Agree,Neutral,Agree,Neutral,Agree,No,Moderately Important,Slightly Important,Moderately Important,Slightly Important,Slightly Important,Moderately Important,Moderately Important,Slightly Important,Yes,Love to order online 
+21,Male,Single,Student,No Income,Graduate,5,12.9783,77.6408,560038,Food delivery apps, Web browser,Lunch, Snacks,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Neutral,Agree,Neutral,Agree,Agree,Disagree,Disagree,Disagree,Disagree,Neutral,Disagree,Agree,Agree,Strongly agree,Yes,Weekend (Sat & Sun),45 minutes,Agree,Neutral,Agree,Strongly Agree,Strongly Agree,Yes,Important,Important,Very Important,Important,Slightly Important,Moderately Important,Important,Important,Yes,I love ordering for my family from swiggy
+24,Female,Single,Self Employeed,25001 to 50000,Post Graduate,3,13.0103,77.5796,560080,Food delivery apps, Direct call,Dinner, Dinner,Veg foods (Breakfast / Lunch / Dinner), Sweets,Disagree,Disagree,Disagree,Neutral,Agree,Agree,Agree,Agree,Disagree,Disagree,Disagree,Disagree,Neutral,Agree,Agree,Neutral,Agree,Disagree,Disagree,Strongly disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Neutral,Neutral,Agree,Neutral,Maybe,Important,Important,Important,Moderately Important,Moderately Important,Important,Important,Important,No,I need faster delivery.
+26,Male,Prefer not to say,Self Employeed,More than 50000,Post Graduate,2,13.0103,77.5796,560080,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Strongly agree,Neutral,Strongly agree,Agree,Agree,Disagree,Disagree,Disagree,Disagree,Disagree,Agree,Disagree,Disagree,Agree,Agree,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Agree,Strongly Agree,Agree,Agree,No,Moderately Important,Moderately Important,Slightly Important,Slightly Important,Slightly Important,Slightly Important,Moderately Important,Moderately Important,Yes,Crazily fast delivery people besides having high traffic
+31,Male,Married,Employee,More than 50000,Ph.D,3,12.9306,77.5434,560085,Food delivery apps, Web browser,Lunch, Snacks,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Neutral,Agree,Agree,Neutral,Agree,Agree,Neutral,Disagree,Disagree,Disagree,Neutral,Disagree,Disagree,Disagree,Disagree,Disagree,Neutral,Disagree,Disagree,No,Anytime (Mon-Sun),30 minutes,Agree,Neutral,Agree,Neutral,Disagree,Yes,Important,Important,Very Important,Important,Moderately Important,Important,Important,Moderately Important,Yes,BEST BEST BEST !! WONDERFUL WONDERFUL !! FAST FAST FAST !!
+29,Male,Single,Employee,25001 to 50000,Graduate,3,13.0641,77.5931,560092,Food delivery apps, Web browser,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Disagree,Disagree,Disagree,Strongly disagree,Disagree,Disagree,Strongly disagree,Agree,Neutral,Agree,Neutral,Agree,Neutral,Agree,Neutral,Agree,Agree,Strongly disagree,Strongly disagree,Strongly disagree,Yes,Anytime (Mon-Sun),45 minutes,Neutral,Agree,Agree,Agree,Strongly Agree,Yes,Important,Very Important,Important,Very Important,Moderately Important,Important,Important,Very Important,No,Need quality food delivery. had worst experience of spilled food
+22,Male,Single,Student,No Income,Graduate,3,13.0158,77.539,560096,Food delivery apps, Web browser,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Neutral,Neutral,Agree,Strongly agree,Agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Strongly agree,Disagree,Disagree,Neutral,Disagree,Disagree,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Agree,Neutral,Agree,Yes,Important,Very Important,Important,Very Important,Important,Very Important,Very Important,Important,Yes,NIL
+24,Male,Single,Student,No Income,Graduate,2,12.9561,77.5921,560027,Direct call, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Strongly agree,Neutral,Neutral,Neutral,Neutral,Neutral,Strongly disagree,Disagree,Neutral,Disagree,Disagree,Neutral,Disagree,Agree,Agree,Agree,Agree,Strongly agree,Neutral,Yes,Weekend (Sat & Sun),30 minutes,Agree,Agree,Agree,Neutral,Neutral,Yes,Important,Important,Moderately Important,Important,Very Important,Very Important,Very Important,Very Important,Yes,NIL
+27,Male,Married,Employee,25001 to 50000,Graduate,2,12.8845,77.6036,560076,Food delivery apps, Web browser,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Disagree,Agree,Neutral,Strongly agree,Neutral,Agree,Disagree,Disagree,Agree,Disagree,Disagree,Disagree,Neutral,Agree,Neutral,Agree,Strongly agree,Agree,Neutral,No,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Agree,Agree,Strongly Agree,Yes,Important,Moderately Important,Important,Moderately Important,Moderately Important,Important,Moderately Important,Moderately Important,Yes,I would avoid restaurants which has less rating 
+23,Female,Single,Student,No Income,Post Graduate,3,12.9369,77.6407,560095,Food delivery apps, Web browser,Snacks, Dinner,Veg foods (Breakfast / Lunch / Dinner), Sweets,Disagree,Neutral,Neutral,Disagree,Disagree,Neutral,Strongly disagree,Agree,Agree,Agree,Agree,Neutral,Disagree,Disagree,Disagree,Neutral,Agree,Disagree,Strongly disagree,Strongly disagree,Yes,Anytime (Mon-Sun),15 minutes,Agree,Agree,Strongly Agree,Agree,Strongly Agree,Maybe,Important,Important,Very Important,Important,Moderately Important,Moderately Important,Important,Important,No,Faster delivery is needed !!
+30,Male,Married,Employee,More than 50000,Post Graduate,6,12.9369,77.6407,560095,Food delivery apps, Walk-in,Lunch, Snacks,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Disagree,Neutral,Neutral,Agree,Neutral,Agree,Agree,Disagree,Neutral,Disagree,Disagree,Disagree,Agree,Neutral,Agree,Disagree,Disagree,Disagree,Yes,Weekend (Sat & Sun),45 minutes,Agree,Neutral,Agree,Agree,Strongly Agree,Yes,Important,Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Yes,My entire family loves swiggy
+22,Female,Single,Student,No Income,Graduate,2,12.9369,77.6407,560095,Food delivery apps, Direct call,Breakfast, Snacks,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Disagree,Disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,No,Anytime (Mon-Sun),More than 60 minutes,Agree,Agree,Strongly Agree,Agree,Disagree,Maybe,Important,Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Yes,Delivery people are working hard. Can't complain
+28,Male,Married,Employee,25001 to 50000,Graduate,3,12.9369,77.6407,560095,Food delivery apps, Web browser,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Strongly agree,Agree,Agree,Agree,Agree,Strongly agree,Disagree,Disagree,Strongly disagree,Disagree,Disagree,Strongly disagree,Strongly disagree,Disagree,Agree,Neutral,Disagree,Disagree,Disagree,Maybe,Weekend (Sat & Sun),60 minutes,Agree,Strongly Agree,Agree,Agree,Strongly Agree,Yes,Very Important,Very Important,Very Important,Very Important,Important,Important,Important,Very Important,Yes,I don't mind about late delivery when it comes from a top rated restaurant believing in its quality
+23,Female,Single,Student,No Income,Post Graduate,2,13.0158,77.539,560096,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Neutral,Agree,Strongly agree,Agree,Agree,Strongly agree,Neutral,Neutral,Neutral,Disagree,Disagree,Disagree,Disagree,Disagree,Strongly disagree,Strongly disagree,Disagree,Disagree,Strongly disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Agree,Strongly Agree,Agree,Agree,Yes,Important,Important,Moderately Important,Important,Moderately Important,Important,Important,Important,Yes,I have no issues related to delivery and I prefer four plus rated restaurant
+30,Male,Married,Self Employeed,More than 50000,Graduate,1,13.0809,77.5565,560097,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Neutral,Disagree,Disagree,Disagree,Disagree,Agree,Agree,Disagree,Agree,Agree,Disagree,Agree,Disagree,Neutral,Agree,Neutral,Agree,Agree,Disagree,Disagree,Yes,Anytime (Mon-Sun),15 minutes,Agree,Agree,Strongly Agree,Agree,Agree,Yes,Important,Important,Very Important,Important,Important,Important,Moderately Important,Important,No,I had misplaced order in the past orders
+21,Male,Single,Student,No Income,Graduate,3,13.0641,77.5931,560092,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Agree,Neutral,Agree,Neutral,Disagree,Disagree,Disagree,Strongly disagree,Strongly disagree,Strongly disagree,Strongly disagree,Disagree,Neutral,Agree,Disagree,Disagree,Strongly disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Agree,Strongly Agree,Agree,Agree,Yes,Important,Important,Important,Important,Important,Important,Important,Important,Yes,Best service
+26,Female,Married,Employee,Below Rs.10000,Graduate,6,12.9859,77.6713,560093,Food delivery apps, Direct call,Dinner, Dinner,Veg foods (Breakfast / Lunch / Dinner), Sweets,Disagree,Disagree,Disagree,Strongly disagree,Strongly disagree,Disagree,Agree,Agree,Agree,Agree,Strongly agree,Agree,Agree,Disagree,Agree,Neutral,Agree,Agree,Disagree,Disagree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Neutral,Agree,Neutral,Agree,Yes,Important,Moderately Important,Important,Important,Important,Very Important,Very Important,Very Important,No,I had bad quality order delivered twice
+25,Male,Single,Self Employeed,More than 50000,School,3,12.9859,77.6713,560093,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Agree,Strongly agree,Strongly agree,Strongly agree,Agree,Disagree,Strongly disagree,Agree,Disagree,Strongly disagree,Disagree,Agree,Agree,Agree,Neutral,Disagree,Disagree,Maybe,Anytime (Mon-Sun),60 minutes,Agree,Disagree,Disagree,Agree,Disagree,No,Moderately Important,Slightly Important,Slightly Important,Slightly Important,Slightly Important,Slightly Important,Unimportant,Slightly Important,Yes,Bad rating doesn't mean that the food tastes bad. Delivery got no problem !
+31,Male,Prefer not to say,Employee,Below Rs.10000,Graduate,1,12.9866,77.4904,560091,Food delivery apps, Direct call,Lunch, Snacks,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Neutral,Disagree,Disagree,Disagree,Disagree,Agree,Agree,Agree,Agree,Agree,Neutral,Disagree,Disagree,Strongly agree,Agree,Agree,Agree,Disagree,Disagree,Disagree,Yes,Weekend (Sat & Sun),15 minutes,Agree,Disagree,Disagree,Neutral,Agree,Yes,Important,Moderately Important,Important,Very Important,Very Important,Very Important,Very Important,Very Important,No,Order delivered to my location are late
+23,Female,Single,Employee,25001 to 50000,Post Graduate,2,12.9847,77.5491,560100,Food delivery apps, Walk-in,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Neutral,Agree,Agree,Strongly agree,Agree,Strongly agree,Disagree,Disagree,Strongly disagree,Disagree,Strongly disagree,Strongly disagree,Disagree,Agree,Agree,Neutral,Agree,Agree,Strongly agree,Yes,Anytime (Mon-Sun),30 minutes,Agree,Strongly Agree,Strongly Agree,Agree,Agree,Yes,Important,Moderately Important,Important,Important,Important,Important,Very Important,Very Important,Yes,My location is pretty well built for food delivery 
+22,Male,Single,Student,Below Rs.10000,Post Graduate,4,12.985,77.5533,560010,Walk-in, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Agree,Agree,Neutral,Neutral,Agree,Strongly agree,Strongly agree,Agree,Strongly agree,Agree,Disagree,Strongly agree,Strongly agree,Neutral,Neutral,Neutral,Disagree,Yes,Weekend (Sat & Sun),30 minutes,Agree,Agree,Agree,Agree,Agree,Yes,Important,Important,Moderately Important,Important,Important,Important,Very Important,Very Important,Yes,NIL
+27,Female,Married,Employee,More than 50000,Post Graduate,2,12.9299,77.6848,560103,Food delivery apps, Walk-in,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Neutral,Agree,Agree,Neutral,Agree,Disagree,Neutral,Agree,Agree,Agree,Agree,Agree,Strongly agree,Strongly agree,Strongly agree,Agree,Strongly agree,Strongly agree,Yes,Anytime (Mon-Sun),45 minutes,Neutral,Agree,Neutral,Neutral,Neutral,Yes,Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Very Important,Yes,NIL
+22,Male,Single,Student,No Income,Graduate,3,12.977,77.5773,560009,Food delivery apps, Direct call,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Strongly agree,Agree,Strongly agree,Agree,Neutral,Agree,Disagree,Agree,Disagree,Agree,Disagree,Strongly agree,Strongly disagree,Neutral,Disagree,Disagree,Strongly agree,Strongly agree,Strongly agree,No,Anytime (Mon-Sun),45 minutes,Agree,Strongly Agree,Disagree,Agree,Strongly Agree,Yes,Very Important,Very Important,Unimportant,Important,Very Important,Very Important,Very Important,Very Important,Yes,Nil
+24,Female,Single,Student,No Income,Post Graduate,3,12.9828,77.6131,560042,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Ice cream / Cool drinks,Strongly agree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Agree,Neutral,Neutral,Neutral,Neutral,Agree,Agree,Agree,Neutral,Agree,Agree,Yes,Weekend (Sat & Sun),45 minutes,Agree,Agree,Agree,Neutral,Disagree,Maybe,Moderately Important,Important,Moderately Important,Slightly Important,Important,Moderately Important,Very Important,Very Important,Yes,NIL
+23,Female,Single,Student,No Income,Post Graduate,2,12.9766,77.5993,560001,Food delivery apps, Direct call,Dinner, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Strongly agree,Agree,Agree,Neutral,Agree,Agree,Agree,Neutral,Agree,Neutral,Neutral,Disagree,Agree,Agree,Agree,Agree,Agree,Strongly agree,Yes,Anytime (Mon-Sun),30 minutes,Disagree,Disagree,Neutral,Agree,Agree,Maybe,Important,Important,Important,Important,Important,Important,Important,Important,Yes,Nil
+23,Female,Single,Student,No Income,Post Graduate,4,12.9854,77.7081,560048,Food delivery apps, Walk-in,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Neutral,Agree,Agree,Neutral,Neutral,Neutral,Neutral,Neutral,Agree,Agree,Agree,Neutral,Agree,Agree,Yes,Weekend (Sat & Sun),45 minutes,Agree,Agree,Agree,Disagree,Disagree,Yes,Moderately Important,Very Important,Moderately Important,Moderately Important,Moderately Important,Moderately Important,Very Important,Very Important,Yes,Nil
+22,Female,Single,Student,No Income,Post Graduate,5,12.985,77.5533,560010,Food delivery apps, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Agree,Agree,Agree,Agree,Agree,Neutral,Agree,Agree,Strongly agree,Agree,Strongly agree,Agree,Agree,Agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Strongly agree,Yes,Anytime (Mon-Sun),45 minutes,Agree,Agree,Strongly Agree,Neutral,Agree,Yes,Important,Very Important,Important,Important,Very Important,Very Important,Very Important,Very Important,Yes,Nil
+23,Male,Single,Student,Below Rs.10000,Post Graduate,2,12.977,77.5773,560009,Food delivery apps, Walk-in,Lunch, Dinner,Non Veg foods (Lunch / Dinner), Veg foods (Breakfast / Lunch / Dinner),Strongly agree,Strongly agree,Strongly agree,Strongly agree,Agree,Agree,Strongly agree,Agree,Strongly agree,Disagree,Agree,Strongly agree,Strongly agree,Neutral,Strongly agree,Strongly agree,Agree,Strongly agree,Strongly agree,Disagree,Maybe,Weekend (Sat & Sun),45 minutes,Strongly Agree,Disagree,Agree,Disagree,Strongly Agree,Yes,Important,Very Important,Important,Very Important,Very Important,Important,Very Important,Very Important,Yes,Language barrier is also one major issue. Moslty these delivery boys are only familiar with Kanada which creates a problem while addressing any issue.
+23,Male,Single,Student,No Income,Post Graduate,5,12.8988,77.5764,560078,Walk-in, Direct call,Snacks, Dinner,Non Veg foods (Lunch / Dinner), Bakery items (snacks),Agree,Neutral,Agree,Agree,Agree,Disagree,Agree,Neutral,Agree,Neutral,Agree,Disagree,Disagree,Neutral,Agree,Neutral,Neutral,Disagree,Strongly disagree,Strongly disagree,Yes,Weekend (Sat & Sun),30 minutes,Agree,Agree,Neutral,Disagree,Agree,Maybe,Slightly Important,Unimportant,Slightly Important,Moderately Important,Moderately Important,Moderately Important,Moderately Important,Slightly Important,Yes,Nil
diff --git a/Online Food Delivery Preferences/Dataset/README.md b/Online Food Delivery Preferences/Dataset/README.md
new file mode 100644
index 000000000..b952e23ee
--- /dev/null
+++ b/Online Food Delivery Preferences/Dataset/README.md	
@@ -0,0 +1,13 @@
+**<h2>Dataset</h2>**
+
+Link : https://www.kaggle.com/datasets/benroshan/online-food-delivery-preferencesbangalore-region/data
+
+`The dataset consists of 388 entries with 55 columns, providing detailed insights into customer preferences and experiences in online food delivery. Key variables include:
+
+1. **Demographics**: Age, gender, marital status, occupation, income, and education levels.
+2. **Geographical Data**: Latitude, longitude, pin codes.
+3. **Service Preferences**: Meal and medium preferences (e.g., "Medium (P1)", "Meal (P1)"), ease, convenience, and time-saving benefits.
+4. **Delivery Experience**: Factors like restaurant choices, payment options, food quality, tracking systems, and road conditions.
+5. **Challenges**: Common issues such as late deliveries, hygiene concerns, wrong orders, missing items, and delivery delays.
+6. **User Feedback**: Reviews, Google Maps accuracy, politeness of delivery personnel, and the influence of ratings.
+7. **Outcome**: Whether this consumer will buy again or not (e.g., "Output").
\ No newline at end of file
diff --git a/Online Food Delivery Preferences/Images/AgeGroup.png b/Online Food Delivery Preferences/Images/AgeGroup.png
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diff --git a/Online Food Delivery Preferences/Model/Model1.ipynb b/Online Food Delivery Preferences/Model/Model1.ipynb
new file mode 100644
index 000000000..086fcad51
--- /dev/null
+++ b/Online Food Delivery Preferences/Model/Model1.ipynb	
@@ -0,0 +1,1614 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        " <h2><b>Get Dataset From Kaggle</h2>"
+      ],
+      "metadata": {
+        "id": "eqECVWbsVHaC"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "g6B0_YipCKQO"
+      },
+      "outputs": [],
+      "source": [
+        "import os # paths to file\n",
+        "os.environ['KAGGLE_USERNAME'] = \"pratibhabalgi\"\n",
+        "os.environ['KAGGLE_KEY'] = \"5653a67e9f7e4d92b1a3c6f2d689ba6f\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!kaggle datasets download benroshan/online-food-delivery-preferencesbangalore-region --force # to get dataset from kaggle"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "nYroHaApFkM9",
+        "outputId": "2ef04bfc-825f-4614-9e89-1732f8f4af48"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Dataset URL: https://www.kaggle.com/datasets/benroshan/online-food-delivery-preferencesbangalore-region\n",
+            "License(s): CC0-1.0\n",
+            "Downloading online-food-delivery-preferencesbangalore-region.zip to /content\n",
+            "  0% 0.00/24.0k [00:00<?, ?B/s]\n",
+            "100% 24.0k/24.0k [00:00<00:00, 9.37MB/s]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!unzip online-food-delivery-preferencesbangalore-region #unzip"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "R1jOA7XVFngB",
+        "outputId": "8a38d1aa-c771-44a3-fdae-e419a17e02bc"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Archive:  online-food-delivery-preferencesbangalore-region.zip\n",
+            "replace onlinedeliverydata.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: n\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        " <h2><b>Importing Important Libraries📚</h2>"
+      ],
+      "metadata": {
+        "id": "HM9hep1qfW99"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd # data processing\n",
+        "import numpy as np # linear algebra\n",
+        "import matplotlib.pyplot as plt # used for creating visualizations like graphs and charts\n",
+        "import seaborn as sns # visualization library"
+      ],
+      "metadata": {
+        "id": "Wefmtuswfnbj"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from scipy import stats  # For Z-score calculation"
+      ],
+      "metadata": {
+        "id": "IVUdVvq_gaIV"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import tensorflow as tf  # TensorFlow for deep learning\n",
+        "\n",
+        "# Scikit-learn for splitting data and encoding labels\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
+        "from sklearn.metrics import classification_report, accuracy_score  # For evaluation metrics\n",
+        "from sklearn.linear_model import LogisticRegression  # Logistic Regression model\n",
+        "\n",
+        "\n",
+        "# TensorFlow Keras model and layers\n",
+        "from tensorflow.keras.models import Sequential  # Sequential model\n",
+        "from tensorflow.keras.layers import Embedding, SimpleRNN, LSTM, GRU, Dense, Dropout, SpatialDropout1D, Bidirectional  # Various layers\n",
+        "\n",
+        "# Regularization\n",
+        "from tensorflow.keras import regularizers  # For L2 regularization\n",
+        "\n",
+        "# Tokenizer and sequence padding for text processing\n",
+        "from tensorflow.keras.preprocessing.text import Tokenizer  # To tokenize text\n",
+        "from tensorflow.keras.preprocessing.sequence import pad_sequences  # To pad sequences for uniform input length\n"
+      ],
+      "metadata": {
+        "id": "BLXE2q4sCLAG"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<h2><b>Data Preprocessing: 🧹 Cleaning & Preparing the Data</h2>"
+      ],
+      "metadata": {
+        "id": "GhFJERebV50G"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data = pd.read_csv('onlinedeliverydata.csv') #read the csv file"
+      ],
+      "metadata": {
+        "id": "x4yKfyTzH9Rq"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Check for missing values\n",
+        "print(\"Missing Values:\\n\", data.isnull().sum())"
+      ],
+      "metadata": {
+        "id": "JxMA_AbSIA3M",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2821fb30-b540-41ea-b979-a6589bcb9858"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Missing Values:\n",
+            " Age                                          0\n",
+            "Gender                                       0\n",
+            "Marital Status                               0\n",
+            "Occupation                                   0\n",
+            "Monthly Income                               0\n",
+            "Educational Qualifications                   0\n",
+            "Family size                                  0\n",
+            "latitude                                     0\n",
+            "longitude                                    0\n",
+            "Pin code                                     0\n",
+            "Medium (P1)                                  0\n",
+            "Medium (P2)                                  0\n",
+            "Meal(P1)                                     0\n",
+            "Meal(P2)                                     0\n",
+            "Perference(P1)                               0\n",
+            "Perference(P2)                               0\n",
+            "Ease and convenient                          0\n",
+            "Time saving                                  0\n",
+            "More restaurant choices                      0\n",
+            "Easy Payment option                          0\n",
+            "More Offers and Discount                     0\n",
+            "Good Food quality                            0\n",
+            "Good Tracking system                         0\n",
+            "Self Cooking                                 0\n",
+            "Health Concern                               0\n",
+            "Late Delivery                                0\n",
+            "Poor Hygiene                                 0\n",
+            "Bad past experience                          0\n",
+            "Unavailability                               0\n",
+            "Unaffordable                                 0\n",
+            "Long delivery time                           0\n",
+            "Delay of delivery person getting assigned    0\n",
+            "Delay of delivery person picking up food     0\n",
+            "Wrong order delivered                        0\n",
+            "Missing item                                 0\n",
+            "Order placed by mistake                      0\n",
+            "Influence of time                            0\n",
+            "Order Time                                   0\n",
+            "Maximum wait time                            0\n",
+            "Residence in busy location                   0\n",
+            "Google Maps Accuracy                         0\n",
+            "Good Road Condition                          0\n",
+            "Low quantity low time                        0\n",
+            "Delivery person ability                      0\n",
+            "Influence of rating                          0\n",
+            "Less Delivery time                           0\n",
+            "High Quality of package                      0\n",
+            "Number of calls                              0\n",
+            "Politeness                                   0\n",
+            "Freshness                                    0\n",
+            "Temperature                                  0\n",
+            "Good Taste                                   0\n",
+            "Good Quantity                                0\n",
+            "Output                                       0\n",
+            "Reviews                                      1\n",
+            "dtype: int64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        " #Fill missing value\n",
+        "data['Reviews'] = data['Reviews'].fillna('Nil')"
+      ],
+      "metadata": {
+        "id": "hQrqhfmBIJBp"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Handle Outliers\n",
+        "z_scores = np.abs(stats.zscore(data[['Age', 'Family size']]))\n",
+        "data = data[(z_scores < 3).all(axis=1)]\n",
+        "print(f\"Shape after removing outliers: {data.shape}\")"
+      ],
+      "metadata": {
+        "id": "X0-9LmTvIRnL",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "26d98a06-ff5b-4c7c-e433-1d321cc981c6"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Shape after removing outliers: (388, 55)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Encode Categorical Data\n",
+        "encoder = LabelEncoder()\n",
+        "categorical_cols = ['Gender', 'Marital Status', 'Occupation', 'Output']\n",
+        "for col in categorical_cols:\n",
+        "    data[col] = encoder.fit_transform(data[col])"
+      ],
+      "metadata": {
+        "id": "IskEF1VwItFs"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(data.head()) # display first 5 rows"
+      ],
+      "metadata": {
+        "id": "qCKaWsC6I9SG",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "3ec4b5d3-5972-420b-8a90-ef96b7fb662b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "   Age  Gender  Marital Status  Occupation  Monthly Income  \\\n",
+            "0   20       0               2           3       No Income   \n",
+            "1   24       0               2           3  Below Rs.10000   \n",
+            "2   22       1               2           3  Below Rs.10000   \n",
+            "3   22       0               2           3       No Income   \n",
+            "4   22       1               2           3  Below Rs.10000   \n",
+            "\n",
+            "  Educational Qualifications  Family size  latitude  longitude  Pin code  ...  \\\n",
+            "0              Post Graduate            4   12.9766    77.5993    560001  ...   \n",
+            "1                   Graduate            3   12.9770    77.5773    560009  ...   \n",
+            "2              Post Graduate            3   12.9551    77.6593    560017  ...   \n",
+            "3                   Graduate            6   12.9473    77.5616    560019  ...   \n",
+            "4              Post Graduate            4   12.9850    77.5533    560010  ...   \n",
+            "\n",
+            "     Less Delivery time High Quality of package       Number of calls  \\\n",
+            "0  Moderately Important    Moderately Important  Moderately Important   \n",
+            "1        Very Important          Very Important        Very Important   \n",
+            "2             Important          Very Important  Moderately Important   \n",
+            "3        Very Important               Important  Moderately Important   \n",
+            "4             Important               Important  Moderately Important   \n",
+            "\n",
+            "             Politeness            Freshness            Temperature  \\\n",
+            "0  Moderately Important  Moderately Important  Moderately Important   \n",
+            "1        Very Important        Very Important        Very Important   \n",
+            "2        Very Important        Very Important             Important   \n",
+            "3        Very Important        Very Important        Very Important   \n",
+            "4             Important             Important             Important   \n",
+            "\n",
+            "            Good Taste          Good Quantity Output  \\\n",
+            "0  Moderately Important  Moderately Important      1   \n",
+            "1        Very Important        Very Important      1   \n",
+            "2        Very Important  Moderately Important      1   \n",
+            "3        Very Important             Important      1   \n",
+            "4        Very Important        Very Important      1   \n",
+            "\n",
+            "                                             Reviews  \n",
+            "0                                              Nil\\n  \n",
+            "1                                                Nil  \n",
+            "2  Many a times payment gateways are an issue, so...  \n",
+            "3                                                nil  \n",
+            "4                                                NIL  \n",
+            "\n",
+            "[5 rows x 55 columns]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# To work with Monthly Income column\n",
+        "# Define a mapping for income ranges to numerical values\n",
+        "income_mapping = {\n",
+        "    'No Income': 0,\n",
+        "    'Below Rs.10000': 5000,\n",
+        "    '10001 to 25000': 17500,\n",
+        "    '25001 to 50000': 37500,\n",
+        "    'More than 50000': 50000\n",
+        "}\n",
+        "\n",
+        "# Ensure the 'Monthly Income' column is of type string\n",
+        "data['Monthly Income'] = data['Monthly Income'].astype(str)\n",
+        "\n",
+        "# Apply the mapping to the 'Monthly Income' column\n",
+        "data['Monthly Income'] = data['Monthly Income'].replace(income_mapping)\n",
+        "\n",
+        "# Check for any unmapped values (in case of typos or unexpected values)\n",
+        "print(data['Monthly Income'].isna().sum())  # To see if any NaNs are left"
+      ],
+      "metadata": {
+        "id": "Gu3Ct7RGJUj2",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "cecb9d6b-1e52-4869-c5f9-15e014d58e31"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-13-634fc36d4474>:15: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
+            "  data['Monthly Income'] = data['Monthly Income'].replace(income_mapping)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<h2><b>Exploratory Data Analysis (EDA): 📊 Visualizing and Analyzing the Data<h2>"
+      ],
+      "metadata": {
+        "id": "i2niVppBWl1j"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Basic statistics of the dataset\n",
+        "print(data.describe())"
+      ],
+      "metadata": {
+        "id": "5DvqKLWeJoH7",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "fe5eece7-8eb1-476e-dafd-74ec953b05b9"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              Age      Gender  Marital Status  Occupation  Monthly Income  \\\n",
+            "count  388.000000  388.000000      388.000000  388.000000      388.000000   \n",
+            "mean    24.628866    0.572165        1.412371    1.902062    17010.309278   \n",
+            "std      2.975593    0.495404        0.895035    1.329722    19959.225799   \n",
+            "min     18.000000    0.000000        0.000000    0.000000        0.000000   \n",
+            "25%     23.000000    0.000000        0.000000    0.000000        0.000000   \n",
+            "50%     24.000000    1.000000        2.000000    3.000000     5000.000000   \n",
+            "75%     26.000000    1.000000        2.000000    3.000000    37500.000000   \n",
+            "max     33.000000    1.000000        2.000000    3.000000    50000.000000   \n",
+            "\n",
+            "       Family size    latitude   longitude       Pin code      Output  \n",
+            "count   388.000000  388.000000  388.000000     388.000000  388.000000  \n",
+            "mean      3.280928   12.972058   77.600160  560040.113402    0.775773  \n",
+            "std       1.351025    0.044489    0.051354      31.399609    0.417611  \n",
+            "min       1.000000   12.865200   77.484200  560001.000000    0.000000  \n",
+            "25%       2.000000   12.936900   77.565275  560010.750000    1.000000  \n",
+            "50%       3.000000   12.977000   77.592100  560033.500000    1.000000  \n",
+            "75%       4.000000   12.997025   77.630900  560068.000000    1.000000  \n",
+            "max       6.000000   13.102000   77.758200  560109.000000    1.000000  \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**⭕ Based On Demography Of Customers**"
+      ],
+      "metadata": {
+        "id": "DWr9x2GoXGhj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Distribution of Age\n",
+        "data['Age_group'] = pd.cut(data['Age'],bins = [15,20,25,30,35], labels = ['15-20y','20-25y','25-30y','30-35y'])\n",
+        "\n",
+        "# Plot bar plot for age group distribution\n",
+        "plt.figure(figsize=(10, 5))\n",
+        "sns.countplot(hue='Age_group', x='Age_group', data=data, palette='coolwarm', legend=False)\n",
+        "\n",
+        "plt.title('Age Group Distribution', fontsize=16)\n",
+        "plt.xlabel('Age Group', fontsize=14)\n",
+        "plt.ylabel('Count', fontsize=14)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "LB_XrpxJLGCj",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 451
+        },
+        "outputId": "e7b95fae-8595-4dc9-b734-a0c1b32331ca"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set the figure size\n",
+        "plt.figure(figsize=(15,10))\n",
+        "\n",
+        "# Plot 1: Gender distribution\n",
+        "plt.subplot(3,2,1)\n",
+        "sns.countplot(data=data,x='Gender')\n",
+        "plt.title('Gender')\n",
+        "\n",
+        "# Plot 2: Marital Status distribution\n",
+        "plt.subplot(3,2,2)\n",
+        "sns.countplot(data=data,x='Marital Status')\n",
+        "plt.title('Marital Status')\n",
+        "\n",
+        "# Plot 3: Occupation distribution\n",
+        "plt.subplot(3,2,3)\n",
+        "sns.countplot(data=data,x='Occupation')\n",
+        "plt.title('Occupation')\n",
+        "\n",
+        "# Plot 4: Monthly Income distribution\n",
+        "plt.subplot(3,2,4)\n",
+        "sns.countplot(data=data,x='Monthly Income')\n",
+        "plt.title('Monthly Income')\n",
+        "\n",
+        "# Plot 5: Family size distribution\n",
+        "plt.subplot(3,2,5)\n",
+        "sns.countplot(data=data,x='Family size')\n",
+        "plt.title('Family size')\n",
+        "\n",
+        "# Plot 6: Educational Qualifications distribution\n",
+        "plt.subplot(3,2,6)\n",
+        "sns.countplot(data=data,x='Educational Qualifications')\n",
+        "plt.title('Educational Qualifications')\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "VLYV_4aeOt28",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 533
+        },
+        "outputId": "d6bc9936-e550-4002-b74e-2f29e57d8a13"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1500x1000 with 6 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Visualize correlations\n",
+        "plt.figure(figsize=(10, 10))\n",
+        "\n",
+        "# Select only numerical features for correlation calculation\n",
+        "numerical_features = data.select_dtypes(include=np.number)\n",
+        "sns.heatmap(numerical_features.corr(), annot=True, cmap='coolwarm')\n",
+        "plt.title('Feature Correlation Matrix')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "jHu7wU9mO1f8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 839
+        },
+        "outputId": "1f426da7-f110-47bf-ac14-2f1b588ff7ee"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x1000 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**⭕ Features that influence users to use online food delivery**"
+      ],
+      "metadata": {
+        "id": "RH2xjSNbYViX"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set the figure size\n",
+        "plt.figure(figsize=(20, 10))\n",
+        "\n",
+        "# Plot 1: Ease and convenient\n",
+        "plt.subplot(3, 3, 1)\n",
+        "sns.countplot(data=data, x='Ease and convenient', hue='Ease and convenient', palette='magma', legend=False)\n",
+        "plt.title('Ease and convenient')\n",
+        "\n",
+        "# Plot 2: Time saving\n",
+        "plt.subplot(3, 3, 2)\n",
+        "sns.countplot(data=data, x='Time saving', hue='Time saving', palette='magma', legend=False)\n",
+        "plt.title('Time saving')\n",
+        "\n",
+        "# Plot 3: More restaurant choices\n",
+        "plt.subplot(3, 3, 3)\n",
+        "sns.countplot(data=data, x='More restaurant choices', hue='More restaurant choices', palette='magma', legend=False)\n",
+        "plt.title('More restaurant choices')\n",
+        "\n",
+        "# Plot 4: Easy Payment option\n",
+        "plt.subplot(3, 3, 4)\n",
+        "sns.countplot(data=data, x='Easy Payment option', hue='Easy Payment option', palette='magma', legend=False)\n",
+        "plt.title('Easy Payment option')\n",
+        "\n",
+        "# Plot 5: More Offers and Discount\n",
+        "plt.subplot(3, 3, 5)\n",
+        "sns.countplot(data=data, x='More Offers and Discount', hue='More Offers and Discount', palette='magma', legend=False)\n",
+        "plt.title('More Offers and Discount')\n",
+        "\n",
+        "# Plot 6: Good Food quality\n",
+        "plt.subplot(3, 3, 6)\n",
+        "sns.countplot(data=data, x='Good Food quality', hue='Good Food quality', palette='magma', legend=False)\n",
+        "plt.title('Good Food quality')\n",
+        "\n",
+        "# Plot 7: Good Tracking system\n",
+        "plt.subplot(3, 3, 7)\n",
+        "sns.countplot(data=data, x='Good Tracking system', hue='Good Tracking system', palette='magma', legend=False)\n",
+        "plt.title('Good Tracking system')\n",
+        "\n",
+        "# Adjust layout to prevent overlap\n",
+        "plt.tight_layout()\n",
+        "\n",
+        "# Show the plot\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "id": "GU8cAhm6UcOb",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 406
+        },
+        "outputId": "57aa87cc-beef-422c-b74b-d3184b1926e3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2000x1000 with 7 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "⭕ **Features that discourage users from using online food delivery**"
+      ],
+      "metadata": {
+        "id": "ZhTAShmnYsSL"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set the figure size\n",
+        "plt.figure(figsize=(20, 10))\n",
+        "\n",
+        "# Plot 1: Self Cooking\n",
+        "plt.subplot(3, 3, 1)\n",
+        "sns.countplot(data=data, x='Self Cooking', hue='Self Cooking', palette='Oranges', legend=False)\n",
+        "plt.title('Self Cooking')\n",
+        "\n",
+        "# Plot 2: Health Concern\n",
+        "plt.subplot(3, 3, 2)\n",
+        "sns.countplot(data=data, x='Health Concern', hue='Health Concern', palette='Oranges', legend=False)\n",
+        "plt.title('Health Concern')\n",
+        "\n",
+        "# Plot 3: Late Delivery\n",
+        "plt.subplot(3, 3, 3)\n",
+        "sns.countplot(data=data, x='Late Delivery', hue='Late Delivery', palette='Oranges', legend=False)\n",
+        "plt.title('Late Delivery')\n",
+        "\n",
+        "# Plot 4: Poor Hygiene\n",
+        "plt.subplot(3, 3, 4)\n",
+        "sns.countplot(data=data, x='Poor Hygiene', hue='Poor Hygiene', palette='Oranges', legend=False)\n",
+        "plt.title('Poor Hygiene')\n",
+        "\n",
+        "# Plot 5: Bad Past Experience\n",
+        "plt.subplot(3, 3, 5)\n",
+        "sns.countplot(data=data, x='Bad past experience', hue='Bad past experience', palette='Oranges', legend=False)\n",
+        "plt.title('Bad past experience')\n",
+        "\n",
+        "# Plot 6: Unavailability\n",
+        "plt.subplot(3, 3, 6)\n",
+        "sns.countplot(data=data, x='Unavailability', hue='Unavailability', palette='Oranges', legend=False)\n",
+        "plt.title('Unavailability')\n",
+        "\n",
+        "# Plot 7: Unaffordable\n",
+        "plt.subplot(3, 3, 7)\n",
+        "sns.countplot(data=data, x='Unaffordable', hue='Unaffordable', palette='Oranges', legend=False)\n",
+        "plt.title('Unaffordable')\n",
+        "\n",
+        "# Adjust layout to prevent overlap\n",
+        "plt.tight_layout()\n",
+        "\n",
+        "# Show the plot\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "id": "SzcGkGCrVX7b",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 406
+        },
+        "outputId": "36bf1475-d0f5-4ec8-db9a-b093668c3d69"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2000x1000 with 7 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "⭕ **Time Factor**"
+      ],
+      "metadata": {
+        "id": "42cC_qa0WxQV"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set the figure size\n",
+        "plt.figure(figsize=(20,10))\n",
+        "\n",
+        "# Plot 1: Distribution of 'Influence of time'\n",
+        "plt.subplot(2,3,1)\n",
+        "sns.histplot(data['Influence of time'], bins=30, kde=True)\n",
+        "plt.title('Influence of time')\n",
+        "plt.xticks(rotation=60)\n",
+        "\n",
+        "# Plot 2: Distribution of 'Maximum wait time'\n",
+        "plt.subplot(2,3,2)\n",
+        "sns.histplot(data['Maximum wait time'], bins=30, kde=True)\n",
+        "plt.title('Maximum wait time')\n",
+        "plt.xticks(rotation=60)\n",
+        "\n",
+        "# Plot 3: Distribution of 'Less Delivery time'\n",
+        "plt.subplot(2,3,3)\n",
+        "sns.histplot(data['Less Delivery time'], bins=30, kde=True)\n",
+        "plt.title('Less Delivery time')\n",
+        "plt.xticks(rotation=60)\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "m8_IHWeNWW6j",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 249
+        },
+        "outputId": "776a0fb7-77fa-4dad-fe13-cd6ff475e690"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2000x1000 with 3 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<h2><b>Training And Testing Models 🤖</h2>"
+      ],
+      "metadata": {
+        "id": "2Hhk6gmmCmc2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<h3><b>Problem 1 : Predict User Behavior</h3>\n",
+        "\n",
+        "*   Model 1: Convolutional Neural Networks (CNN)\n",
+        "\n",
+        "*   Model 2: Recurrent Neural Networks (RNN)\n",
+        "*   Model 3: RNN+LSTM Hybrid Model"
+      ],
+      "metadata": {
+        "id": "XFZWiWcqCxcS"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define features and target\n",
+        "X = data[['Age', 'Family size', 'Monthly Income', 'Occupation']]\n",
+        "y = data['Output']\n",
+        "\n",
+        "# Split data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+        "\n",
+        "# Scale the feature data\n",
+        "scaler = StandardScaler()\n",
+        "X_train = scaler.fit_transform(X_train)\n",
+        "X_test = scaler.transform(X_test)"
+      ],
+      "metadata": {
+        "id": "_4h8WUuxcTE7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Model 1: Convolutional Neural Networks (CNN)**"
+      ],
+      "metadata": {
+        "id": "s1QEt4sqG-Ng"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define a CNN model\n",
+        "model_cnn = tf.keras.Sequential([\n",
+        "    tf.keras.layers.InputLayer(input_shape=(X_train.shape[1], 1)),\n",
+        "    tf.keras.layers.Conv1D(64, kernel_size=3, activation='relu', padding='same'),\n",
+        "    tf.keras.layers.MaxPooling1D(pool_size=2),\n",
+        "    tf.keras.layers.Conv1D(32, kernel_size=3, activation='relu', padding='same'),\n",
+        "    tf.keras.layers.MaxPooling1D(pool_size=2),\n",
+        "    tf.keras.layers.Flatten(),\n",
+        "    tf.keras.layers.Dense(64, activation='relu'),\n",
+        "    tf.keras.layers.Dropout(0.5),  # Increased dropout rate\n",
+        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
+        "])\n",
+        "\n",
+        "# Compile the model\n",
+        "model_cnn.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
+        "\n",
+        "# Reshape data for CNN input\n",
+        "X_train_cnn = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
+        "X_test_cnn = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n",
+        "\n",
+        "# Train the model\n",
+        "history_cnn = model_cnn.fit(X_train_cnn, y_train, epochs=50, validation_data=(X_test_cnn, y_test), batch_size=16)\n",
+        "\n",
+        "# Evaluate the model\n",
+        "loss, accuracy_cnn = model_cnn.evaluate(X_test_cnn, y_test)\n",
+        "print(f'CNN Accuracy: {accuracy_cnn*100:.2f}%')\n",
+        "\n",
+        "# Predict on test data\n",
+        "y_pred = (model_cnn.predict(X_test) > 0.5).astype(\"int32\")  # Threshold at 0.5\n",
+        "\n",
+        "# Accuracy and Loss curves\n",
+        "result=pd.DataFrame(history_cnn.history)\n",
+        "fig, ax=plt.subplots(nrows=1, ncols=2,figsize=(18,6))\n",
+        "ax=ax.flatten()\n",
+        "ax[0].plot(result[['accuracy','val_accuracy']])\n",
+        "ax[0].set_title(\"Accuracy\")\n",
+        "ax[1].plot(result[['loss','val_loss']])\n",
+        "ax[1].set_title(\"Loss\")\n",
+        "\n",
+        "plt.show()\n",
+        "\n",
+        "# Classification Report\n",
+        "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))"
+      ],
+      "metadata": {
+        "id": "mtULLuggc9w8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "93d199a1-507b-468d-ee11-f5bd479bcc72"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/input_layer.py:26: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 33ms/step - accuracy: 0.6526 - loss: 0.6567 - val_accuracy: 0.8462 - val_loss: 0.5009\n",
+            "Epoch 2/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7430 - loss: 0.5632 - val_accuracy: 0.8462 - val_loss: 0.4108\n",
+            "Epoch 3/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7755 - loss: 0.5158 - val_accuracy: 0.8462 - val_loss: 0.3987\n",
+            "Epoch 4/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7514 - loss: 0.5168 - val_accuracy: 0.8462 - val_loss: 0.3985\n",
+            "Epoch 5/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7422 - loss: 0.5333 - val_accuracy: 0.8462 - val_loss: 0.3916\n",
+            "Epoch 6/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7731 - loss: 0.5095 - val_accuracy: 0.8462 - val_loss: 0.3846\n",
+            "Epoch 7/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7387 - loss: 0.5478 - val_accuracy: 0.8462 - val_loss: 0.3798\n",
+            "Epoch 8/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7973 - loss: 0.4611 - val_accuracy: 0.8462 - val_loss: 0.3645\n",
+            "Epoch 9/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7466 - loss: 0.5656 - val_accuracy: 0.8333 - val_loss: 0.3817\n",
+            "Epoch 10/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7336 - loss: 0.5065 - val_accuracy: 0.8333 - val_loss: 0.3607\n",
+            "Epoch 11/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7811 - loss: 0.4789 - val_accuracy: 0.8462 - val_loss: 0.3552\n",
+            "Epoch 12/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7745 - loss: 0.5053 - val_accuracy: 0.8462 - val_loss: 0.3554\n",
+            "Epoch 13/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7839 - loss: 0.5041 - val_accuracy: 0.8462 - val_loss: 0.3572\n",
+            "Epoch 14/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7564 - loss: 0.4766 - val_accuracy: 0.8333 - val_loss: 0.3409\n",
+            "Epoch 15/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7938 - loss: 0.5138 - val_accuracy: 0.8462 - val_loss: 0.3471\n",
+            "Epoch 16/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8052 - loss: 0.4634 - val_accuracy: 0.8590 - val_loss: 0.3468\n",
+            "Epoch 17/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7878 - loss: 0.5051 - val_accuracy: 0.8462 - val_loss: 0.3431\n",
+            "Epoch 18/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7840 - loss: 0.4787 - val_accuracy: 0.8462 - val_loss: 0.3459\n",
+            "Epoch 19/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7962 - loss: 0.4754 - val_accuracy: 0.8590 - val_loss: 0.3405\n",
+            "Epoch 20/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8070 - loss: 0.4673 - val_accuracy: 0.8590 - val_loss: 0.3351\n",
+            "Epoch 21/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8227 - loss: 0.4631 - val_accuracy: 0.8590 - val_loss: 0.3231\n",
+            "Epoch 22/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7769 - loss: 0.5043 - val_accuracy: 0.8590 - val_loss: 0.3171\n",
+            "Epoch 23/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7985 - loss: 0.4428 - val_accuracy: 0.8462 - val_loss: 0.3285\n",
+            "Epoch 24/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8038 - loss: 0.4464 - val_accuracy: 0.8590 - val_loss: 0.3336\n",
+            "Epoch 25/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8251 - loss: 0.4122 - val_accuracy: 0.8462 - val_loss: 0.3318\n",
+            "Epoch 26/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8008 - loss: 0.4557 - val_accuracy: 0.8718 - val_loss: 0.3113\n",
+            "Epoch 27/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8063 - loss: 0.4224 - val_accuracy: 0.8846 - val_loss: 0.3220\n",
+            "Epoch 28/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7919 - loss: 0.4749 - val_accuracy: 0.8846 - val_loss: 0.3070\n",
+            "Epoch 29/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7927 - loss: 0.4344 - val_accuracy: 0.8590 - val_loss: 0.3049\n",
+            "Epoch 30/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7771 - loss: 0.4715 - val_accuracy: 0.8974 - val_loss: 0.3110\n",
+            "Epoch 31/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8305 - loss: 0.4193 - val_accuracy: 0.8974 - val_loss: 0.3194\n",
+            "Epoch 32/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8258 - loss: 0.4356 - val_accuracy: 0.9103 - val_loss: 0.2905\n",
+            "Epoch 33/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8259 - loss: 0.4501 - val_accuracy: 0.8846 - val_loss: 0.2903\n",
+            "Epoch 34/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8147 - loss: 0.4200 - val_accuracy: 0.8974 - val_loss: 0.2926\n",
+            "Epoch 35/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8203 - loss: 0.4041 - val_accuracy: 0.8974 - val_loss: 0.2970\n",
+            "Epoch 36/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8080 - loss: 0.4526 - val_accuracy: 0.8974 - val_loss: 0.2744\n",
+            "Epoch 37/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8130 - loss: 0.4173 - val_accuracy: 0.9103 - val_loss: 0.3102\n",
+            "Epoch 38/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8310 - loss: 0.4019 - val_accuracy: 0.8718 - val_loss: 0.3018\n",
+            "Epoch 39/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8043 - loss: 0.4624 - val_accuracy: 0.8974 - val_loss: 0.3084\n",
+            "Epoch 40/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8160 - loss: 0.4123 - val_accuracy: 0.8846 - val_loss: 0.2947\n",
+            "Epoch 41/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8589 - loss: 0.3899 - val_accuracy: 0.9103 - val_loss: 0.3341\n",
+            "Epoch 42/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8201 - loss: 0.3989 - val_accuracy: 0.8718 - val_loss: 0.2710\n",
+            "Epoch 43/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8064 - loss: 0.4329 - val_accuracy: 0.8974 - val_loss: 0.2788\n",
+            "Epoch 44/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7901 - loss: 0.4564 - val_accuracy: 0.8718 - val_loss: 0.2791\n",
+            "Epoch 45/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8381 - loss: 0.3954 - val_accuracy: 0.9103 - val_loss: 0.2716\n",
+            "Epoch 46/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8547 - loss: 0.3767 - val_accuracy: 0.8974 - val_loss: 0.2675\n",
+            "Epoch 47/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8238 - loss: 0.4119 - val_accuracy: 0.9103 - val_loss: 0.2781\n",
+            "Epoch 48/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8530 - loss: 0.3858 - val_accuracy: 0.9103 - val_loss: 0.2934\n",
+            "Epoch 49/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8392 - loss: 0.3818 - val_accuracy: 0.9103 - val_loss: 0.2667\n",
+            "Epoch 50/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8265 - loss: 0.4103 - val_accuracy: 0.8974 - val_loss: 0.2601\n",
+            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9018 - loss: 0.2444 \n",
+            "CNN Accuracy: 89.74%\n",
+            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1800x600 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Classification Report:\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.83      0.42      0.56        12\n",
+            "           1       0.90      0.98      0.94        66\n",
+            "\n",
+            "    accuracy                           0.90        78\n",
+            "   macro avg       0.87      0.70      0.75        78\n",
+            "weighted avg       0.89      0.90      0.88        78\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Display sample predictions\n",
+        "print(\"First 5 Predictions:\", y_pred[:5].flatten())\n",
+        "print(\"First 5 Actual Values:\", y_test[:5].values)"
+      ],
+      "metadata": {
+        "id": "gWZD_qtA0pA3",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "63aaeade-c06a-421b-8d95-d11639488c1c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "First 5 Predictions: [1 1 1 1 0]\n",
+            "First 5 Actual Values: [0 1 1 1 0]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Model 2: Recurrent Neural Networks (RNN)**"
+      ],
+      "metadata": {
+        "id": "VYwITQLlHz0a"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define the RNN model\n",
+        "model = Sequential([\n",
+        "     tf.keras.layers.Reshape((X_train.shape[1], 1), input_shape=(X_train.shape[1],)),\n",
+        "        # More layers and more units:\n",
+        "        SimpleRNN(128, activation='relu', return_sequences=True),  # Increased units\n",
+        "        SimpleRNN(64, activation='relu', return_sequences=True),  # Added another layer\n",
+        "        SimpleRNN(32, activation='relu'),\n",
+        "        Dense(1, activation='sigmoid')\n",
+        "])\n",
+        "\n",
+        "# Compile the model\n",
+        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
+        "\n",
+        "# Train the model\n",
+        "history_rnn=model.fit(X_train, y_train, epochs=50, batch_size=16, validation_data=(X_test, y_test))\n",
+        "\n",
+        "# Evaluate the model\n",
+        "loss, accuracy = model.evaluate(X_test, y_test)\n",
+        "print(f'RNN Test Accuracy: {accuracy:.2f}')\n",
+        "\n",
+        "# Predict on test data\n",
+        "y_pred = (model.predict(X_test) > 0.5).astype(\"int32\")  # Threshold at 0.5\n",
+        "\n",
+        "# Accuracy and Loss curves\n",
+        "result=pd.DataFrame(history_rnn.history)\n",
+        "fig, ax=plt.subplots(nrows=1, ncols=2,figsize=(18,6))\n",
+        "ax=ax.flatten()\n",
+        "ax[0].plot(result[['accuracy','val_accuracy']])\n",
+        "ax[0].set_title(\"Accuracy\")\n",
+        "ax[1].plot(result[['loss','val_loss']])\n",
+        "ax[1].set_title(\"Loss\")\n",
+        "\n",
+        "plt.show()\n",
+        "\n",
+        "# Classification Report\n",
+        "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))"
+      ],
+      "metadata": {
+        "id": "PRoBtbBicnaJ",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "471951e1-c08c-4639-efb0-a118a94cea7a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/50\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/reshaping/reshape.py:39: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+            "  super().__init__(**kwargs)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 34ms/step - accuracy: 0.7256 - loss: 0.6209 - val_accuracy: 0.8462 - val_loss: 0.3693\n",
+            "Epoch 2/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.7593 - loss: 0.5072 - val_accuracy: 0.8462 - val_loss: 0.3711\n",
+            "Epoch 3/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.7802 - loss: 0.4625 - val_accuracy: 0.8462 - val_loss: 0.3458\n",
+            "Epoch 4/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.7866 - loss: 0.4435 - val_accuracy: 0.8846 - val_loss: 0.3450\n",
+            "Epoch 5/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8001 - loss: 0.4527 - val_accuracy: 0.8846 - val_loss: 0.3199\n",
+            "Epoch 6/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.7869 - loss: 0.4457 - val_accuracy: 0.8846 - val_loss: 0.3026\n",
+            "Epoch 7/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8154 - loss: 0.4319 - val_accuracy: 0.8718 - val_loss: 0.3366\n",
+            "Epoch 8/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8092 - loss: 0.4390 - val_accuracy: 0.8846 - val_loss: 0.2973\n",
+            "Epoch 9/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.7733 - loss: 0.4623 - val_accuracy: 0.8718 - val_loss: 0.2996\n",
+            "Epoch 10/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8083 - loss: 0.4231 - val_accuracy: 0.8718 - val_loss: 0.3026\n",
+            "Epoch 11/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.7775 - loss: 0.4610 - val_accuracy: 0.8718 - val_loss: 0.3013\n",
+            "Epoch 12/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8396 - loss: 0.3874 - val_accuracy: 0.8846 - val_loss: 0.3357\n",
+            "Epoch 13/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.7463 - loss: 0.4509 - val_accuracy: 0.8718 - val_loss: 0.3069\n",
+            "Epoch 14/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8333 - loss: 0.3956 - val_accuracy: 0.8718 - val_loss: 0.3383\n",
+            "Epoch 15/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8160 - loss: 0.4096 - val_accuracy: 0.8718 - val_loss: 0.3070\n",
+            "Epoch 16/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8235 - loss: 0.3685 - val_accuracy: 0.8846 - val_loss: 0.3227\n",
+            "Epoch 17/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8069 - loss: 0.3997 - val_accuracy: 0.8590 - val_loss: 0.3137\n",
+            "Epoch 18/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8106 - loss: 0.3913 - val_accuracy: 0.8718 - val_loss: 0.3021\n",
+            "Epoch 19/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8413 - loss: 0.3471 - val_accuracy: 0.8846 - val_loss: 0.3203\n",
+            "Epoch 20/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8520 - loss: 0.3613 - val_accuracy: 0.8974 - val_loss: 0.2949\n",
+            "Epoch 21/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8301 - loss: 0.3858 - val_accuracy: 0.8846 - val_loss: 0.3041\n",
+            "Epoch 22/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8403 - loss: 0.3456 - val_accuracy: 0.8718 - val_loss: 0.3191\n",
+            "Epoch 23/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.7914 - loss: 0.4020 - val_accuracy: 0.8846 - val_loss: 0.2830\n",
+            "Epoch 24/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8237 - loss: 0.3597 - val_accuracy: 0.9231 - val_loss: 0.3214\n",
+            "Epoch 25/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8377 - loss: 0.3866 - val_accuracy: 0.8846 - val_loss: 0.3109\n",
+            "Epoch 26/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8184 - loss: 0.3675 - val_accuracy: 0.9103 - val_loss: 0.2856\n",
+            "Epoch 27/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8242 - loss: 0.3660 - val_accuracy: 0.8846 - val_loss: 0.3100\n",
+            "Epoch 28/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8124 - loss: 0.3637 - val_accuracy: 0.9103 - val_loss: 0.2953\n",
+            "Epoch 29/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8629 - loss: 0.3192 - val_accuracy: 0.9103 - val_loss: 0.3370\n",
+            "Epoch 30/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8681 - loss: 0.3263 - val_accuracy: 0.9103 - val_loss: 0.2997\n",
+            "Epoch 31/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8559 - loss: 0.3186 - val_accuracy: 0.8974 - val_loss: 0.3280\n",
+            "Epoch 32/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8149 - loss: 0.3722 - val_accuracy: 0.8718 - val_loss: 0.3262\n",
+            "Epoch 33/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.8428 - loss: 0.3375 - val_accuracy: 0.9103 - val_loss: 0.3119\n",
+            "Epoch 34/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - accuracy: 0.8566 - loss: 0.3182 - val_accuracy: 0.9103 - val_loss: 0.3220\n",
+            "Epoch 35/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.8561 - loss: 0.3379 - val_accuracy: 0.8718 - val_loss: 0.3273\n",
+            "Epoch 36/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.8685 - loss: 0.3177 - val_accuracy: 0.9103 - val_loss: 0.3067\n",
+            "Epoch 37/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 22ms/step - accuracy: 0.8304 - loss: 0.3369 - val_accuracy: 0.9231 - val_loss: 0.2962\n",
+            "Epoch 38/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - accuracy: 0.8459 - loss: 0.3523 - val_accuracy: 0.9103 - val_loss: 0.3155\n",
+            "Epoch 39/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.8308 - loss: 0.3328 - val_accuracy: 0.9231 - val_loss: 0.3121\n",
+            "Epoch 40/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.8599 - loss: 0.3109 - val_accuracy: 0.9103 - val_loss: 0.3079\n",
+            "Epoch 41/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.8492 - loss: 0.3157 - val_accuracy: 0.9231 - val_loss: 0.3222\n",
+            "Epoch 42/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8342 - loss: 0.3305 - val_accuracy: 0.8974 - val_loss: 0.3523\n",
+            "Epoch 43/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8431 - loss: 0.3156 - val_accuracy: 0.8718 - val_loss: 0.3166\n",
+            "Epoch 44/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8743 - loss: 0.2834 - val_accuracy: 0.8974 - val_loss: 0.3397\n",
+            "Epoch 45/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8426 - loss: 0.3276 - val_accuracy: 0.9231 - val_loss: 0.2976\n",
+            "Epoch 46/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8586 - loss: 0.3180 - val_accuracy: 0.8974 - val_loss: 0.3463\n",
+            "Epoch 47/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8481 - loss: 0.3506 - val_accuracy: 0.9231 - val_loss: 0.3290\n",
+            "Epoch 48/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8745 - loss: 0.2903 - val_accuracy: 0.9231 - val_loss: 0.3062\n",
+            "Epoch 49/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8930 - loss: 0.2640 - val_accuracy: 0.9231 - val_loss: 0.3455\n",
+            "Epoch 50/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8752 - loss: 0.2945 - val_accuracy: 0.9231 - val_loss: 0.3174\n",
+            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9381 - loss: 0.2624 \n",
+            "RNN Test Accuracy: 0.92\n",
+            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 201ms/step\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1800x600 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Classification Report:\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.88      0.58      0.70        12\n",
+            "           1       0.93      0.98      0.96        66\n",
+            "\n",
+            "    accuracy                           0.92        78\n",
+            "   macro avg       0.90      0.78      0.83        78\n",
+            "weighted avg       0.92      0.92      0.92        78\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Display sample predictions\n",
+        "print(\"First 5 Predictions:\", y_pred[:5].flatten())\n",
+        "print(\"First 5 Actual Values:\", y_test[:5].values)"
+      ],
+      "metadata": {
+        "id": "3TX5QPxM0PWz",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0ac09795-f866-4013-e83e-3ec59e1b2e5f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "First 5 Predictions: [1 1 1 1 0]\n",
+            "First 5 Actual Values: [0 1 1 1 0]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Model 3: RNN+LSTM Hybrid Model**"
+      ],
+      "metadata": {
+        "id": "fOtxhUKiIhNq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Reshape the input data -> make it compatible with LSTM\n",
+        "X_train_rnn = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))\n",
+        "X_test_rnn = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))\n",
+        "\n",
+        "# Define the RNN model with LSTM layers\n",
+        "model_rnn = tf.keras.Sequential([\n",
+        "    tf.keras.layers.InputLayer(input_shape=(X_train_rnn.shape[1], 1)),\n",
+        "    tf.keras.layers.LSTM(128, return_sequences=True),\n",
+        "    tf.keras.layers.Dropout(0.3),\n",
+        "    tf.keras.layers.LSTM(64, return_sequences=False),\n",
+        "    tf.keras.layers.Dropout(0.3),\n",
+        "    tf.keras.layers.Dense(32, activation='relu'),\n",
+        "    tf.keras.layers.Dense(1, activation='sigmoid')  # Binary classification output\n",
+        "])\n",
+        "\n",
+        "# Compile the model\n",
+        "model_rnn.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
+        "\n",
+        "# Train the RNN model\n",
+        "history_lstm = model_rnn.fit(X_train_rnn, y_train, epochs=50, validation_data=(X_test_rnn, y_test), batch_size=16)\n",
+        "\n",
+        "# Evaluate the model\n",
+        "loss, accuracy_rnn = model_rnn.evaluate(X_test_rnn, y_test)\n",
+        "print(f'RNN with LSTM Accuracy: {accuracy_rnn*100:.2f}%')\n",
+        "\n",
+        "# Predict on test data\n",
+        "y_pred = (model_rnn.predict(X_test) > 0.5).astype(\"int32\")  # Threshold at 0.5\n",
+        "\n",
+        "# Accuracy and Loss curves\n",
+        "result=pd.DataFrame(history_lstm.history)\n",
+        "fig, ax=plt.subplots(nrows=1, ncols=2,figsize=(18,6))\n",
+        "ax=ax.flatten()\n",
+        "ax[0].plot(result[['accuracy','val_accuracy']])\n",
+        "ax[0].set_title(\"Accuracy\")\n",
+        "ax[1].plot(result[['loss','val_loss']])\n",
+        "ax[1].set_title(\"Loss\")\n",
+        "\n",
+        "plt.show()\n",
+        "\n",
+        "# Classification Report\n",
+        "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))"
+      ],
+      "metadata": {
+        "id": "MomPJxVbw94_",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "2a03545c-1e93-483c-b7f9-15d30071f5e3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/50\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/input_layer.py:26: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 35ms/step - accuracy: 0.7152 - loss: 0.6765 - val_accuracy: 0.8462 - val_loss: 0.5642\n",
+            "Epoch 2/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.7323 - loss: 0.5621 - val_accuracy: 0.8462 - val_loss: 0.3956\n",
+            "Epoch 3/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.7911 - loss: 0.4821 - val_accuracy: 0.8462 - val_loss: 0.3999\n",
+            "Epoch 4/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.7566 - loss: 0.5295 - val_accuracy: 0.8462 - val_loss: 0.3968\n",
+            "Epoch 5/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - accuracy: 0.7902 - loss: 0.4933 - val_accuracy: 0.8462 - val_loss: 0.3722\n",
+            "Epoch 6/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.7395 - loss: 0.5226 - val_accuracy: 0.8462 - val_loss: 0.3784\n",
+            "Epoch 7/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 22ms/step - accuracy: 0.7625 - loss: 0.5107 - val_accuracy: 0.8462 - val_loss: 0.3588\n",
+            "Epoch 8/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - accuracy: 0.7621 - loss: 0.4996 - val_accuracy: 0.8462 - val_loss: 0.3651\n",
+            "Epoch 9/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.7274 - loss: 0.5450 - val_accuracy: 0.8462 - val_loss: 0.3504\n",
+            "Epoch 10/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.7636 - loss: 0.5111 - val_accuracy: 0.8462 - val_loss: 0.3509\n",
+            "Epoch 11/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.7541 - loss: 0.5305 - val_accuracy: 0.8718 - val_loss: 0.3529\n",
+            "Epoch 12/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.7805 - loss: 0.4617 - val_accuracy: 0.8718 - val_loss: 0.3433\n",
+            "Epoch 13/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.8024 - loss: 0.4520 - val_accuracy: 0.8718 - val_loss: 0.3475\n",
+            "Epoch 14/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.8085 - loss: 0.4538 - val_accuracy: 0.9231 - val_loss: 0.3468\n",
+            "Epoch 15/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.7972 - loss: 0.5114 - val_accuracy: 0.8974 - val_loss: 0.3463\n",
+            "Epoch 16/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.7829 - loss: 0.5214 - val_accuracy: 0.9359 - val_loss: 0.3186\n",
+            "Epoch 17/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.7689 - loss: 0.5021 - val_accuracy: 0.9359 - val_loss: 0.3176\n",
+            "Epoch 18/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.8128 - loss: 0.4848 - val_accuracy: 0.9231 - val_loss: 0.3220\n",
+            "Epoch 19/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.8028 - loss: 0.4939 - val_accuracy: 0.9103 - val_loss: 0.3243\n",
+            "Epoch 20/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.8397 - loss: 0.4455 - val_accuracy: 0.9359 - val_loss: 0.3051\n",
+            "Epoch 21/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.7988 - loss: 0.4825 - val_accuracy: 0.8974 - val_loss: 0.3339\n",
+            "Epoch 22/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.8165 - loss: 0.4835 - val_accuracy: 0.9231 - val_loss: 0.2967\n",
+            "Epoch 23/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.8117 - loss: 0.4590 - val_accuracy: 0.9231 - val_loss: 0.3125\n",
+            "Epoch 24/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.8119 - loss: 0.4493 - val_accuracy: 0.9231 - val_loss: 0.3102\n",
+            "Epoch 25/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.7973 - loss: 0.4771 - val_accuracy: 0.9231 - val_loss: 0.3094\n",
+            "Epoch 26/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.8041 - loss: 0.4983 - val_accuracy: 0.9359 - val_loss: 0.3004\n",
+            "Epoch 27/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.8304 - loss: 0.4399 - val_accuracy: 0.9359 - val_loss: 0.2992\n",
+            "Epoch 28/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.8406 - loss: 0.4385 - val_accuracy: 0.8974 - val_loss: 0.3218\n",
+            "Epoch 29/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.8074 - loss: 0.4931 - val_accuracy: 0.9103 - val_loss: 0.3116\n",
+            "Epoch 30/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.8138 - loss: 0.4693 - val_accuracy: 0.9359 - val_loss: 0.2917\n",
+            "Epoch 31/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.8019 - loss: 0.4923 - val_accuracy: 0.9231 - val_loss: 0.2865\n",
+            "Epoch 32/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.8135 - loss: 0.4727 - val_accuracy: 0.9103 - val_loss: 0.3027\n",
+            "Epoch 33/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.8006 - loss: 0.4436 - val_accuracy: 0.9359 - val_loss: 0.2848\n",
+            "Epoch 34/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.8330 - loss: 0.4469 - val_accuracy: 0.9103 - val_loss: 0.3169\n",
+            "Epoch 35/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.8258 - loss: 0.4617 - val_accuracy: 0.9359 - val_loss: 0.2901\n",
+            "Epoch 36/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.8073 - loss: 0.4605 - val_accuracy: 0.9359 - val_loss: 0.2960\n",
+            "Epoch 37/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.8375 - loss: 0.4418 - val_accuracy: 0.9103 - val_loss: 0.3071\n",
+            "Epoch 38/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.8304 - loss: 0.4581 - val_accuracy: 0.9359 - val_loss: 0.2853\n",
+            "Epoch 39/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.8167 - loss: 0.4860 - val_accuracy: 0.9231 - val_loss: 0.2938\n",
+            "Epoch 40/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.8203 - loss: 0.4516 - val_accuracy: 0.9231 - val_loss: 0.2967\n",
+            "Epoch 41/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.7818 - loss: 0.5071 - val_accuracy: 0.9231 - val_loss: 0.2930\n",
+            "Epoch 42/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.8133 - loss: 0.4760 - val_accuracy: 0.9359 - val_loss: 0.2847\n",
+            "Epoch 43/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.8116 - loss: 0.4938 - val_accuracy: 0.9231 - val_loss: 0.3129\n",
+            "Epoch 44/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.8284 - loss: 0.4615 - val_accuracy: 0.9359 - val_loss: 0.2764\n",
+            "Epoch 45/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.8082 - loss: 0.4880 - val_accuracy: 0.9359 - val_loss: 0.2926\n",
+            "Epoch 46/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - accuracy: 0.7958 - loss: 0.5050 - val_accuracy: 0.9231 - val_loss: 0.2825\n",
+            "Epoch 47/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.8067 - loss: 0.4771 - val_accuracy: 0.9359 - val_loss: 0.2920\n",
+            "Epoch 48/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.8113 - loss: 0.4856 - val_accuracy: 0.9359 - val_loss: 0.2970\n",
+            "Epoch 49/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.8246 - loss: 0.4613 - val_accuracy: 0.9359 - val_loss: 0.2970\n",
+            "Epoch 50/50\n",
+            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.7822 - loss: 0.5122 - val_accuracy: 0.9231 - val_loss: 0.2947\n",
+            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9342 - loss: 0.2785 \n",
+            "RNN with LSTM Accuracy: 92.31%\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:tensorflow:5 out of the last 7 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x7f0660148ee0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\r\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 312ms/step"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:tensorflow:6 out of the last 9 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x7f0660148ee0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 180ms/step\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1800x600 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Classification Report:\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.88      0.58      0.70        12\n",
+            "           1       0.93      0.98      0.96        66\n",
+            "\n",
+            "    accuracy                           0.92        78\n",
+            "   macro avg       0.90      0.78      0.83        78\n",
+            "weighted avg       0.92      0.92      0.92        78\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Display sample predictions\n",
+        "print(\"First 5 Predictions:\", y_pred[:5].flatten())\n",
+        "print(\"First 5 Actual Values:\", y_test[:5].values)\n"
+      ],
+      "metadata": {
+        "id": "CmTx8L7f96bg",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0cd1d5f1-6d9e-4212-a7ef-faa428a33d32"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "First 5 Predictions: [1 1 1 1 0]\n",
+            "First 5 Actual Values: [0 1 1 1 0]\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Online Food Delivery Preferences/Model/Model2.ipynb b/Online Food Delivery Preferences/Model/Model2.ipynb
new file mode 100644
index 000000000..991603c46
--- /dev/null
+++ b/Online Food Delivery Preferences/Model/Model2.ipynb	
@@ -0,0 +1,1638 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        " <h2><b>Get Dataset From Kaggle</h2>"
+      ],
+      "metadata": {
+        "id": "eqECVWbsVHaC"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "g6B0_YipCKQO"
+      },
+      "outputs": [],
+      "source": [
+        "import os # paths to file\n",
+        "os.environ['KAGGLE_USERNAME'] = \"pratibhabalgi\"\n",
+        "os.environ['KAGGLE_KEY'] = \"5653a67e9f7e4d92b1a3c6f2d689ba6f\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!kaggle datasets download benroshan/online-food-delivery-preferencesbangalore-region --force # to get dataset from kaggle"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "nYroHaApFkM9",
+        "outputId": "2ef04bfc-825f-4614-9e89-1732f8f4af48"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Dataset URL: https://www.kaggle.com/datasets/benroshan/online-food-delivery-preferencesbangalore-region\n",
+            "License(s): CC0-1.0\n",
+            "Downloading online-food-delivery-preferencesbangalore-region.zip to /content\n",
+            "  0% 0.00/24.0k [00:00<?, ?B/s]\n",
+            "100% 24.0k/24.0k [00:00<00:00, 9.37MB/s]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!unzip online-food-delivery-preferencesbangalore-region #unzip"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "R1jOA7XVFngB",
+        "outputId": "8a38d1aa-c771-44a3-fdae-e419a17e02bc"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Archive:  online-food-delivery-preferencesbangalore-region.zip\n",
+            "replace onlinedeliverydata.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: n\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        " <h2><b>Importing Important Libraries📚</h2>"
+      ],
+      "metadata": {
+        "id": "HM9hep1qfW99"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd # data processing\n",
+        "import numpy as np # linear algebra\n",
+        "import matplotlib.pyplot as plt # used for creating visualizations like graphs and charts\n",
+        "import seaborn as sns # visualization library"
+      ],
+      "metadata": {
+        "id": "Wefmtuswfnbj"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from scipy import stats  # For Z-score calculation"
+      ],
+      "metadata": {
+        "id": "IVUdVvq_gaIV"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import tensorflow as tf  # TensorFlow for deep learning\n",
+        "\n",
+        "# Scikit-learn for splitting data and encoding labels\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
+        "from sklearn.metrics import classification_report, accuracy_score  # For evaluation metrics\n",
+        "from sklearn.linear_model import LogisticRegression  # Logistic Regression model\n",
+        "\n",
+        "\n",
+        "# TensorFlow Keras model and layers\n",
+        "from tensorflow.keras.models import Sequential  # Sequential model\n",
+        "from tensorflow.keras.layers import Embedding, SimpleRNN, LSTM, GRU, Dense, Dropout, SpatialDropout1D, Bidirectional  # Various layers\n",
+        "\n",
+        "# Regularization\n",
+        "from tensorflow.keras import regularizers  # For L2 regularization\n",
+        "\n",
+        "# Tokenizer and sequence padding for text processing\n",
+        "from tensorflow.keras.preprocessing.text import Tokenizer  # To tokenize text\n",
+        "from tensorflow.keras.preprocessing.sequence import pad_sequences  # To pad sequences for uniform input length\n"
+      ],
+      "metadata": {
+        "id": "BLXE2q4sCLAG"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<h2><b>Data Preprocessing: 🧹 Cleaning & Preparing the Data</h2>"
+      ],
+      "metadata": {
+        "id": "GhFJERebV50G"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data = pd.read_csv('onlinedeliverydata.csv') #read the csv file"
+      ],
+      "metadata": {
+        "id": "x4yKfyTzH9Rq"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Check for missing values\n",
+        "print(\"Missing Values:\\n\", data.isnull().sum())"
+      ],
+      "metadata": {
+        "id": "JxMA_AbSIA3M",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2821fb30-b540-41ea-b979-a6589bcb9858"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Missing Values:\n",
+            " Age                                          0\n",
+            "Gender                                       0\n",
+            "Marital Status                               0\n",
+            "Occupation                                   0\n",
+            "Monthly Income                               0\n",
+            "Educational Qualifications                   0\n",
+            "Family size                                  0\n",
+            "latitude                                     0\n",
+            "longitude                                    0\n",
+            "Pin code                                     0\n",
+            "Medium (P1)                                  0\n",
+            "Medium (P2)                                  0\n",
+            "Meal(P1)                                     0\n",
+            "Meal(P2)                                     0\n",
+            "Perference(P1)                               0\n",
+            "Perference(P2)                               0\n",
+            "Ease and convenient                          0\n",
+            "Time saving                                  0\n",
+            "More restaurant choices                      0\n",
+            "Easy Payment option                          0\n",
+            "More Offers and Discount                     0\n",
+            "Good Food quality                            0\n",
+            "Good Tracking system                         0\n",
+            "Self Cooking                                 0\n",
+            "Health Concern                               0\n",
+            "Late Delivery                                0\n",
+            "Poor Hygiene                                 0\n",
+            "Bad past experience                          0\n",
+            "Unavailability                               0\n",
+            "Unaffordable                                 0\n",
+            "Long delivery time                           0\n",
+            "Delay of delivery person getting assigned    0\n",
+            "Delay of delivery person picking up food     0\n",
+            "Wrong order delivered                        0\n",
+            "Missing item                                 0\n",
+            "Order placed by mistake                      0\n",
+            "Influence of time                            0\n",
+            "Order Time                                   0\n",
+            "Maximum wait time                            0\n",
+            "Residence in busy location                   0\n",
+            "Google Maps Accuracy                         0\n",
+            "Good Road Condition                          0\n",
+            "Low quantity low time                        0\n",
+            "Delivery person ability                      0\n",
+            "Influence of rating                          0\n",
+            "Less Delivery time                           0\n",
+            "High Quality of package                      0\n",
+            "Number of calls                              0\n",
+            "Politeness                                   0\n",
+            "Freshness                                    0\n",
+            "Temperature                                  0\n",
+            "Good Taste                                   0\n",
+            "Good Quantity                                0\n",
+            "Output                                       0\n",
+            "Reviews                                      1\n",
+            "dtype: int64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        " #Fill missing value\n",
+        "data['Reviews'] = data['Reviews'].fillna('Nil')"
+      ],
+      "metadata": {
+        "id": "hQrqhfmBIJBp"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Handle Outliers\n",
+        "z_scores = np.abs(stats.zscore(data[['Age', 'Family size']]))\n",
+        "data = data[(z_scores < 3).all(axis=1)]\n",
+        "print(f\"Shape after removing outliers: {data.shape}\")"
+      ],
+      "metadata": {
+        "id": "X0-9LmTvIRnL",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "26d98a06-ff5b-4c7c-e433-1d321cc981c6"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Shape after removing outliers: (388, 55)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Encode Categorical Data\n",
+        "encoder = LabelEncoder()\n",
+        "categorical_cols = ['Gender', 'Marital Status', 'Occupation', 'Output']\n",
+        "for col in categorical_cols:\n",
+        "    data[col] = encoder.fit_transform(data[col])"
+      ],
+      "metadata": {
+        "id": "IskEF1VwItFs"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(data.head()) # display first 5 rows"
+      ],
+      "metadata": {
+        "id": "qCKaWsC6I9SG",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "3ec4b5d3-5972-420b-8a90-ef96b7fb662b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "   Age  Gender  Marital Status  Occupation  Monthly Income  \\\n",
+            "0   20       0               2           3       No Income   \n",
+            "1   24       0               2           3  Below Rs.10000   \n",
+            "2   22       1               2           3  Below Rs.10000   \n",
+            "3   22       0               2           3       No Income   \n",
+            "4   22       1               2           3  Below Rs.10000   \n",
+            "\n",
+            "  Educational Qualifications  Family size  latitude  longitude  Pin code  ...  \\\n",
+            "0              Post Graduate            4   12.9766    77.5993    560001  ...   \n",
+            "1                   Graduate            3   12.9770    77.5773    560009  ...   \n",
+            "2              Post Graduate            3   12.9551    77.6593    560017  ...   \n",
+            "3                   Graduate            6   12.9473    77.5616    560019  ...   \n",
+            "4              Post Graduate            4   12.9850    77.5533    560010  ...   \n",
+            "\n",
+            "     Less Delivery time High Quality of package       Number of calls  \\\n",
+            "0  Moderately Important    Moderately Important  Moderately Important   \n",
+            "1        Very Important          Very Important        Very Important   \n",
+            "2             Important          Very Important  Moderately Important   \n",
+            "3        Very Important               Important  Moderately Important   \n",
+            "4             Important               Important  Moderately Important   \n",
+            "\n",
+            "             Politeness            Freshness            Temperature  \\\n",
+            "0  Moderately Important  Moderately Important  Moderately Important   \n",
+            "1        Very Important        Very Important        Very Important   \n",
+            "2        Very Important        Very Important             Important   \n",
+            "3        Very Important        Very Important        Very Important   \n",
+            "4             Important             Important             Important   \n",
+            "\n",
+            "            Good Taste          Good Quantity Output  \\\n",
+            "0  Moderately Important  Moderately Important      1   \n",
+            "1        Very Important        Very Important      1   \n",
+            "2        Very Important  Moderately Important      1   \n",
+            "3        Very Important             Important      1   \n",
+            "4        Very Important        Very Important      1   \n",
+            "\n",
+            "                                             Reviews  \n",
+            "0                                              Nil\\n  \n",
+            "1                                                Nil  \n",
+            "2  Many a times payment gateways are an issue, so...  \n",
+            "3                                                nil  \n",
+            "4                                                NIL  \n",
+            "\n",
+            "[5 rows x 55 columns]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# To work with Monthly Income column\n",
+        "# Define a mapping for income ranges to numerical values\n",
+        "income_mapping = {\n",
+        "    'No Income': 0,\n",
+        "    'Below Rs.10000': 5000,\n",
+        "    '10001 to 25000': 17500,\n",
+        "    '25001 to 50000': 37500,\n",
+        "    'More than 50000': 50000\n",
+        "}\n",
+        "\n",
+        "# Ensure the 'Monthly Income' column is of type string\n",
+        "data['Monthly Income'] = data['Monthly Income'].astype(str)\n",
+        "\n",
+        "# Apply the mapping to the 'Monthly Income' column\n",
+        "data['Monthly Income'] = data['Monthly Income'].replace(income_mapping)\n",
+        "\n",
+        "# Check for any unmapped values (in case of typos or unexpected values)\n",
+        "print(data['Monthly Income'].isna().sum())  # To see if any NaNs are left"
+      ],
+      "metadata": {
+        "id": "Gu3Ct7RGJUj2",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "cecb9d6b-1e52-4869-c5f9-15e014d58e31"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-13-634fc36d4474>:15: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
+            "  data['Monthly Income'] = data['Monthly Income'].replace(income_mapping)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<h2><b>Exploratory Data Analysis (EDA): 📊 Visualizing and Analyzing the Data<h2>"
+      ],
+      "metadata": {
+        "id": "i2niVppBWl1j"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Basic statistics of the dataset\n",
+        "print(data.describe())"
+      ],
+      "metadata": {
+        "id": "5DvqKLWeJoH7",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "fe5eece7-8eb1-476e-dafd-74ec953b05b9"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              Age      Gender  Marital Status  Occupation  Monthly Income  \\\n",
+            "count  388.000000  388.000000      388.000000  388.000000      388.000000   \n",
+            "mean    24.628866    0.572165        1.412371    1.902062    17010.309278   \n",
+            "std      2.975593    0.495404        0.895035    1.329722    19959.225799   \n",
+            "min     18.000000    0.000000        0.000000    0.000000        0.000000   \n",
+            "25%     23.000000    0.000000        0.000000    0.000000        0.000000   \n",
+            "50%     24.000000    1.000000        2.000000    3.000000     5000.000000   \n",
+            "75%     26.000000    1.000000        2.000000    3.000000    37500.000000   \n",
+            "max     33.000000    1.000000        2.000000    3.000000    50000.000000   \n",
+            "\n",
+            "       Family size    latitude   longitude       Pin code      Output  \n",
+            "count   388.000000  388.000000  388.000000     388.000000  388.000000  \n",
+            "mean      3.280928   12.972058   77.600160  560040.113402    0.775773  \n",
+            "std       1.351025    0.044489    0.051354      31.399609    0.417611  \n",
+            "min       1.000000   12.865200   77.484200  560001.000000    0.000000  \n",
+            "25%       2.000000   12.936900   77.565275  560010.750000    1.000000  \n",
+            "50%       3.000000   12.977000   77.592100  560033.500000    1.000000  \n",
+            "75%       4.000000   12.997025   77.630900  560068.000000    1.000000  \n",
+            "max       6.000000   13.102000   77.758200  560109.000000    1.000000  \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**⭕ Based On Demography Of Customers**"
+      ],
+      "metadata": {
+        "id": "DWr9x2GoXGhj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Distribution of Age\n",
+        "data['Age_group'] = pd.cut(data['Age'],bins = [15,20,25,30,35], labels = ['15-20y','20-25y','25-30y','30-35y'])\n",
+        "\n",
+        "# Plot bar plot for age group distribution\n",
+        "plt.figure(figsize=(10, 5))\n",
+        "sns.countplot(hue='Age_group', x='Age_group', data=data, palette='coolwarm', legend=False)\n",
+        "\n",
+        "plt.title('Age Group Distribution', fontsize=16)\n",
+        "plt.xlabel('Age Group', fontsize=14)\n",
+        "plt.ylabel('Count', fontsize=14)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "LB_XrpxJLGCj",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 451
+        },
+        "outputId": "e7b95fae-8595-4dc9-b734-a0c1b32331ca"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set the figure size\n",
+        "plt.figure(figsize=(15,10))\n",
+        "\n",
+        "# Plot 1: Gender distribution\n",
+        "plt.subplot(3,2,1)\n",
+        "sns.countplot(data=data,x='Gender')\n",
+        "plt.title('Gender')\n",
+        "\n",
+        "# Plot 2: Marital Status distribution\n",
+        "plt.subplot(3,2,2)\n",
+        "sns.countplot(data=data,x='Marital Status')\n",
+        "plt.title('Marital Status')\n",
+        "\n",
+        "# Plot 3: Occupation distribution\n",
+        "plt.subplot(3,2,3)\n",
+        "sns.countplot(data=data,x='Occupation')\n",
+        "plt.title('Occupation')\n",
+        "\n",
+        "# Plot 4: Monthly Income distribution\n",
+        "plt.subplot(3,2,4)\n",
+        "sns.countplot(data=data,x='Monthly Income')\n",
+        "plt.title('Monthly Income')\n",
+        "\n",
+        "# Plot 5: Family size distribution\n",
+        "plt.subplot(3,2,5)\n",
+        "sns.countplot(data=data,x='Family size')\n",
+        "plt.title('Family size')\n",
+        "\n",
+        "# Plot 6: Educational Qualifications distribution\n",
+        "plt.subplot(3,2,6)\n",
+        "sns.countplot(data=data,x='Educational Qualifications')\n",
+        "plt.title('Educational Qualifications')\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "VLYV_4aeOt28",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 533
+        },
+        "outputId": "d6bc9936-e550-4002-b74e-2f29e57d8a13"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1500x1000 with 6 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Visualize correlations\n",
+        "plt.figure(figsize=(10, 10))\n",
+        "\n",
+        "# Select only numerical features for correlation calculation\n",
+        "numerical_features = data.select_dtypes(include=np.number)\n",
+        "sns.heatmap(numerical_features.corr(), annot=True, cmap='coolwarm')\n",
+        "plt.title('Feature Correlation Matrix')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "jHu7wU9mO1f8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 839
+        },
+        "outputId": "1f426da7-f110-47bf-ac14-2f1b588ff7ee"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x1000 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**⭕ Features that influence users to use online food delivery**"
+      ],
+      "metadata": {
+        "id": "RH2xjSNbYViX"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set the figure size\n",
+        "plt.figure(figsize=(20, 10))\n",
+        "\n",
+        "# Plot 1: Ease and convenient\n",
+        "plt.subplot(3, 3, 1)\n",
+        "sns.countplot(data=data, x='Ease and convenient', hue='Ease and convenient', palette='magma', legend=False)\n",
+        "plt.title('Ease and convenient')\n",
+        "\n",
+        "# Plot 2: Time saving\n",
+        "plt.subplot(3, 3, 2)\n",
+        "sns.countplot(data=data, x='Time saving', hue='Time saving', palette='magma', legend=False)\n",
+        "plt.title('Time saving')\n",
+        "\n",
+        "# Plot 3: More restaurant choices\n",
+        "plt.subplot(3, 3, 3)\n",
+        "sns.countplot(data=data, x='More restaurant choices', hue='More restaurant choices', palette='magma', legend=False)\n",
+        "plt.title('More restaurant choices')\n",
+        "\n",
+        "# Plot 4: Easy Payment option\n",
+        "plt.subplot(3, 3, 4)\n",
+        "sns.countplot(data=data, x='Easy Payment option', hue='Easy Payment option', palette='magma', legend=False)\n",
+        "plt.title('Easy Payment option')\n",
+        "\n",
+        "# Plot 5: More Offers and Discount\n",
+        "plt.subplot(3, 3, 5)\n",
+        "sns.countplot(data=data, x='More Offers and Discount', hue='More Offers and Discount', palette='magma', legend=False)\n",
+        "plt.title('More Offers and Discount')\n",
+        "\n",
+        "# Plot 6: Good Food quality\n",
+        "plt.subplot(3, 3, 6)\n",
+        "sns.countplot(data=data, x='Good Food quality', hue='Good Food quality', palette='magma', legend=False)\n",
+        "plt.title('Good Food quality')\n",
+        "\n",
+        "# Plot 7: Good Tracking system\n",
+        "plt.subplot(3, 3, 7)\n",
+        "sns.countplot(data=data, x='Good Tracking system', hue='Good Tracking system', palette='magma', legend=False)\n",
+        "plt.title('Good Tracking system')\n",
+        "\n",
+        "# Adjust layout to prevent overlap\n",
+        "plt.tight_layout()\n",
+        "\n",
+        "# Show the plot\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "id": "GU8cAhm6UcOb",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 406
+        },
+        "outputId": "57aa87cc-beef-422c-b74b-d3184b1926e3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2000x1000 with 7 Axes>"
+            ],
+            "image/png": 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/ySNHjhhXV1dz/Phxuz5NmjQxMTExtxVzbvLwww+byZMnG2P+/eC/5557bL9mHDJkiLn//vvt+v/f//1fuuRRkt2vp7KyrbLyHpBbFNTXW6dOnUzz5s3t2jp06JBp4njmzJlb/pr0RlWqVDFTp041xhizd+9eI8ls377dNn///v1GUrrEccCAAXbjNGnSxO7Xg8YY89///tcEBwcbY/793/L29k6X/JctW9a89957WY73btwseezQoYOpVKmSMcb+f6lfv36mcePGti90t7J48WLj7+9vN+6Nv4zt3Llzun144/9pVrbXrbY54CjkldmvoH7OZYS88u6QV+Zu5CKOVVDfO/mOgLyKnDL7FdT3xYyQU94dcsqsKaivOXIRx+H7Qe7DPbpzqTFjxmjevHnau3evXfuPP/6ouXPnytPT0/aIiopSWlqaDh06dNfPW7VqVRUpUsSubefOnWrVqpVKliwpLy8vNWjQQJJ09OjRu36+nNa+fXudOHFCy5cvV7NmzbRhwwbVqlVLc+fOveWypUqVUvHixW3Te/fuVWhoqEJDQ21tlStXlq+vr91+K126tLy8vGzTwcHBOn36tCTpjz/+UEpKih588EHbfB8fH1WoUOGmsTz77LOaM2eOJOnUqVNauXKlunfvnmn/1NRUvfHGG6patar8/Pzk6empr7/+2rYPbyeOOnXq2E3f6n/y559/Vmpqqu677z67Phs3btTBgwdvup651b59+/T999+rU6dOkqRChQqpQ4cOmj17tm3+Aw88YLfM9dv2miJFiqhatWq26axsq+x+D3Ckgvp627t3r8LDw+3aIiIiMu3v5+enrl27KioqSq1atdI777yjkydP2uYnJSXp5ZdfVqVKleTr6ytPT0/t3bvX9vrdt2+fChUqpFq1atmWKVeunIoVK5buuTJ6/Y4aNcru/6lnz546efKkLl26pB9//FFJSUny9/e363Po0KFc8fo1xshisaRr79q1q3bv3q0KFSqof//++uabb+zmr1mzRk2aNNG9994rLy8vPf300zpz5owuXbok6d9teuNrNqPX8I3/p1nZXrfa5kB2IK/MHgX1c4680rHIK/M2cpHbV1DfO/mOgPyAnDJ7FNT3RXJKxyKnzLqC+pojF8kZfD9wjkLODgAZq1+/vqKiohQTE6OuXbva2pOSkvTcc8+pf//+6ZYpWbKkJMliscgYYzcvJSUlS8/r4eFhN33x4kVFRUUpKipKCxYsUPHixXX06FFFRUXl2ZvZu7m56dFHH9Wjjz6qoUOH6tlnn9Xw4cPttnNGbtw2WVW4cGG7aYvForS0tDsa65pnnnlGr776quLi4vTdd98pLCxMjzzySKb9x40bp3feeUeTJ09W1apV5eHhoQEDBtzRPrxxO9zqf/Knn36Sq6urdu7cKVdXV7v5np6et/38ucHs2bN19epVhYSE2NqMMbJarXr33XezPI67u7vdB19SUtItt1VW3gNyk4L4ersTc+bMUf/+/bVq1SotWrRIr7/+ulavXq2HHnpIL7/8slavXq3x48erXLlycnd31+OPP+6w1+/IkSPVrl27dH3d3NyUlJSk4OBgbdiwId18X1/f235+R9u7d6/CwsLStdeqVUuHDh3SypUrtWbNGj355JOKjIzUkiVLdPjwYbVs2VK9e/fWm2++KT8/P3377bfq0aOHrly5oqJFi2b5+TPanrfaXrfa5kB2IK/MPgXxc4680rHIK/M2cpE7UxDfO+8E3xGQ25BTZp+C+L5ITulY5JS3pyC+5u4Eucjt4/uBc1DozsXefvtt1ahRw+7XO7Vq1dKvv/6qcuXKZbpc8eLF7X5ds3//frtfXlz7FWRqauotY/jtt9905swZvf3227ZfJu3YseO21yU3q1y5spYtW2abLly4cJa2TaVKlXTs2DEdO3bMtm1+/fVXJSQkqHLlyll67jJlyqhw4cLavn277YP//Pnz+v3331W/fv1Ml/P391fbtm01Z84cxcXFqVu3bjd9ni1btqhNmzb6z3/+I0lKS0vT77//bovzTuOQbv0/WbNmTaWmpur06dMO/7B1hqtXr2r+/PmaMGGCmjZtajevbdu2+uijj1ShQgV99dVXdvO2b99+y7Gzsq2y8h6QmxWE11ulSpW0bds2u7atW7feMr6aNWuqZs2aiomJUUREhBYuXKiHHnpIW7ZsUdeuXfXYY49J+jf5OHz4sG25ChUq6OrVq/rhhx9Uu3ZtSdKBAwd07ty5Wz5nrVq1tG/fvkz/n2rVqqX4+HgVKlRIpUuXvuV4OWndunX6+eefNXDgwAzne3t7q0OHDurQoYMef/xxNWvWTGfPntXOnTuVlpamCRMmyMXl3wvbfPLJJ3bLVqhQId1rNiuv4axsr1ttcyC7kFfmjILwOUde6TjklXkbuYjjFIT3Tr4jIL8gp8wZBeF9kZzSccgp715BeM2Ri2Q/vh84D4XuXKxq1arq3LmzpkyZYmsbMmSIHnroIfXt21fPPvusPDw89Ouvv2r16tW2X2c1btxY7777riIiIpSamqohQ4bY/WooICBA7u7uWrVqlUqUKCE3Nzf5+PhkGEPJkiVVpEgRTZ06Vc8//7x++eUXvfHGG9m74tnkzJkzeuKJJ9S9e3dVq1ZNXl5e2rFjh8aOHas2bdrY+pUuXVpr165V3bp1ZbVaM7ychiRFRkba9tHkyZN19epVvfDCC2rQoEG6y21kxsvLS126dNErr7wiPz8/BQQEaPjw4XJxccnwEhfXe/bZZ9WyZUulpqaqS5cuN+1bvnx5LVmyRN99952KFSumiRMn6tSpU7YP3LuJ41b/k/fdd586d+6sZ555RhMmTFDNmjX1119/ae3atapWrZpatGiRpW2VW6xYsULnzp1Tjx490r1u2rdvr9mzZ+uTTz7RxIkTNWTIEPXo0UO7d++2Xf7mZtszK9sqK+8BuUFBfr31799fdevW1fjx49WmTRt9/fXXWrVqVab9Dx06pFmzZql169YKCQnRvn37tH//fj3zzDOS/n39fvbZZ2rVqpUsFouGDh1q96vPihUrKjIyUr169dKMGTNUuHBhvfTSS+l+hZuRYcOGqWXLlipZsqQef/xxubi46Mcff9Qvv/yi0aNHKzIyUhEREWrbtq3Gjh2r++67TydOnNCXX36pxx57LMvb/m4lJycrPj5eqampOnXqlFatWqXY2Fi1bNnStp2uN3HiRAUHB6tmzZpycXHR4sWLFRQUJF9fX5UrV04pKSmaOnWqWrVqpS1btmjmzJl2y/fr10/169fXxIkT1apVK61bt04rV6685fbMyva61TYHsgt5pWMV5M858krHIa/MO8hFHKMgv3fyHQH5BTmlYxXk90VySschp8y6gvyaIxdxLL4f5DJOuzs40snoJvaHDh0yRYoUMdfvqu+//948+uijxtPT03h4eJhq1aqZN9980zb/+PHjpmnTpsbDw8OUL1/efPXVV8bHx8fMmTPH1uf99983oaGhxsXFxTRo0CDT5zfGmIULF5rSpUsbq9VqIiIizPLly40k88MPPxhjjFm/fr2RZM6dO+egLZE9Ll++bF599VVTq1Yt4+PjY4oWLWoqVKhgXn/9dXPp0iVbv+XLl5ty5cqZQoUKmVKlShljjBk+fLipXr16ujGPHDliWrdubTw8PIyXl5d54oknTHx8vG1+RstNmjTJNq4xxiQmJpqnnnrKFC1a1AQFBZmJEyeaBx980Lz66qu2PqVKlTKTJk2yGyctLc2UKlXKNG/e/JbrfubMGdOmTRvj6elpAgICzOuvv26eeeYZu/19p3EYc+v/yStXrphhw4aZ0qVLm8KFC5vg4GDz2GOPmZ9++umWsec2LVu2zHSbb9u2zUgyP/74o/n8889NuXLljNVqNQ0bNjQzZswwksw///xjjDFmzpw5xsfHJ90YWdlWt9reuUFBfr0ZY8zs2bNNiRIljLu7u2nVqpUZP3683f6+Ptb4+HjTtm1bExwcbIoUKWJKlSplhg0bZlJTU40x/34ONGrUyLi7u5vQ0FDz7rvvmgYNGpgXX3zRNt6JEydMdHS0sVqtplSpUmbhwoUmICDAzJw509ZHklm6dGm6WFetWmUefvhh4+7ubry9vc2DDz5oZs2aZbfN+vXrZ0JCQkzhwoVNaGio6dy5szl69GiWtsXd6tKli5FkJJlChQqZ4sWLm8jISPPhhx/atpEx9us3a9YsU6NGDePh4WG8vb1NkyZNzK5du2x9J06caIKDg427u7uJiooy8+fPT/c5NmvWLHPvvfcad3d307ZtWzN69GgTFBRkm5/Z/2lWttettjngCOSV2asgf86RVzoOeWXeQC7iOAX5vdMYviMgbyKnzF4F+X2RnNJxyCmzriC/5owhF3EUvh/kPhZjbrhBCgCnunjxou69915NmDBBPXr0yLRfUlKS7r33Xs2ZMyfD+yfkVBzImjfffFMzZ87UsWPHnB0KrpNbXm+O9ueffyo0NFRr1qxRkyZNnB1OvtCzZ0/99ttv2rx5s7NDAYAsyy2fc+SVjkVeWTCRi+Sc3PLe6Wh8RwBwp3LL+yI5pWORU+ZeueU152jkIo7F94P/j0uXA072ww8/6LffftODDz6o8+fPa9SoUZJkd7mU66Wlpenvv//WhAkT5Ovrq9atWzslDtzc9OnT9cADD8jf319btmzRuHHj1LdvX2eHVeDlltebo61bt05JSUmqWrWqTp48qcGDB6t06dK3vG8VMjd+/Hg9+uij8vDw0MqVKzVv3jxNnz7d2WEBwE3lls858krHIq8smMhFck5uee90NL4jALhTueV9kZzSscgpc6/c8ppzNHIRx+L7QeYodAO5wPjx47Vv3z4VKVJEtWvX1ubNm3XPPfdk2Pfo0aMKCwtTiRIlNHfuXBUq5LiX8e3EgZvbv3+/Ro8erbNnz6pkyZJ66aWXFBMT4+ywoNzzenOklJQUvfbaa/rjjz/k5eWlhx9+WAsWLLC75xluz/fff6+xY8fqwoULKlOmjKZMmaJnn33W2WEBwC3lls858krHIa8smMhFclZuee90JL4jALgbueV9kZzSccgpc7fc8ppzJHIRx+L7Qea4dDkAAAAAAAAAAAAAIE9xcXYAAAAAAAAAAAAAAADcDgrdAAAAAAAAAAAAAIA8hUI3AAAAAAAAAAAAACBPodANAAAAAAAAAAAAAMhTKHQDgINZLBYtW7bM2WFku7lz58rX19fZYQAAADhd165d1bZtW2eH4XDkewAAAMhtNmzYIIvFooSEhLsap6AcwwXyOwrdALJd165dZbFY0j2aNWvm7NBwFzp06KDff//doWM6KlEFAABwlIzy2OsfI0aM0DvvvKO5c+c6O1SHy458DwAAIDtcO/74/PPPp5vXp08fWSwWde3aNecDyyYNGzbUgAEDnB3GbctNcZ88eVLR0dHODgPAXSrk7AAAFAzNmjXTnDlz7NqsVquTooEjuLu7y93d3dlhAAAAZKuTJ0/a/l60aJGGDRumffv22do8PT3l6enpjNCyHfkeAADIS0JDQ/Xxxx9r0qRJthzm8uXLWrhwoUqWLHlXYxtjlJqaqkKF7r6kcuXKFRUpUuSux8kpeS3erAoKCnJ2CAAcgDO6AeQIq9WqoKAgu0exYsVs8ydOnKiqVavKw8NDoaGheuGFF5SUlGSbf+TIEbVq1UrFihWTh4eHqlSpoq+++so2/5dfflF0dLQ8PT0VGBiop59+Wn///Xem8Zw5c0adOnXSvffeq6JFi6pq1ar66KOP7Po0bNhQ/fv31+DBg+Xn56egoCCNGDHCrs/+/ftVv359ubm5qXLlylq9evUtt0VaWprGjh2rcuXKyWq1qmTJknrzzTdt83/++Wc1btxY7u7u8vf3V69evey2xbVLY44fP17BwcHy9/dXnz59lJKSIkl67bXXFB4enu55q1evrlGjRtmmP/jgA1WqVElubm6qWLGipk+fbpt3+PBhWSwWffbZZ2rUqJGKFi2q6tWrKy4uztYno0tZfv7556pVq5bc3NxUpkwZjRw5UlevXrXNt1gs+uCDD/TYY4+paNGiKl++vJYvX257zkaNGkmSihUrlu9+aQsAAPKm6/NXHx8fWSwWuzZPT890ly5v2LCh+vXrpwEDBqhYsWIKDAzU+++/r4sXL6pbt27y8vJSuXLltHLlSrvnut2c9mY5cmpqqnr06KGwsDC5u7urQoUKeuedd2zLfvPNN3Jzc0t3JZ0XX3xRjRs3lpQ+3xsxYoRq1Kih//73vypdurR8fHzUsWNHXbhwwdbnwoUL6ty5szw8PBQcHKxJkyblqjN3AABA/lWrVi2Fhobqs88+s7V99tlnKlmypGrWrGnXNzk5Wf3791dAQIDc3NxUr149bd++3Tb/2lUHV65cqdq1a8tqterbb79VWlqaYmNjbTlW9erVtWTJkpvGVbp0ab3xxht65pln5O3trV69ekmSvv32Wz3yyCNyd3dXaGio+vfvr4sXL9qWmz59uv4fe/cdXUW19nH8l0IKCUkMJUVCkSJIl2aAKy1KF66AgLkQiqBUARXkXqkqCEgXaSrFCxZEUOHSQVCkdwFphiKQgEISaQGS/f7ByrwckkASkpyU72etWTp79sw8cyY5PJlnZk+pUqXk5uYmPz8/tWnTRtLda4ObNm3SlClTrFGGTp069dD8T0r6iepWrVrZXINLLt7BgwerdOnSyps3r5544gkNHTrUuh4pPTxXTC7upMTGxmrw4MEKCgqSq6urSpYsqU8//dSmz+7du1WtWjXlzZtXtWrVsrkZVZJmzJihEiVKyMXFRU8++aQ+//xzm+X3D13+xx9/qEOHDvL19ZWHh4eqVaum7du3W8sfdN3TGKMRI0aoSJEicnV1VWBgoPr165fksQFIXxS6AWQJjo6Omjp1qg4dOqT58+drw4YNGjRokLW8d+/eio2N1ebNm3Xw4EGNHTvWenImKipKDRo0UJUqVbRr1y6tWrVKkZGReumll5Ld382bN1W1alWtWLFCv/76q3r06KGOHTtqx44dNv3mz58vDw8Pbd++XePGjdOoUaOsYnZ8fLxefPFFubi4aPv27Zo5c6YGDx780GMdMmSIPvjgAw0dOlSHDx/WokWL5OfnJ0m6du2aGjVqpMcee0w7d+7U4sWLtW7dOvXp08dmGxs3btTJkye1ceNGzZ8/X/PmzbOGywwNDdWOHTt08uRJq/+hQ4d04MABvfzyy5KkhQsXatiwYXr//fd15MgRjR49WkOHDtX8+fNt9vOf//xHb775pvbt26fSpUurQ4cONoXre/3000/q1KmTXn/9dR0+fFizZs3SvHnzbIr4kjRy5Ei99NJLOnDggJo2barQ0FBdvnxZQUFBWrJkiSTp6NGjunDhQqJkHAAAILuYP3++ChQooB07dqhv377q2bOn2rZtq1q1amnPnj16/vnn1bFjR12/fl1S2nLaB+XI8fHxKly4sBYvXqzDhw9r2LBh+ve//62vv/5aktSwYUP5+PhY+Zd0tzj+1VdfKTQ0NNl9njx5UsuWLdPy5cu1fPlybdq0SR988IG1fODAgdqyZYu+//57rV27Vj/99JP27NnzSJ8lAABASnXt2tVmVMnPPvtMXbp0SdRv0KBBWrJkiebPn689e/aoZMmSatSokS5fvmzT7+2339YHH3ygI0eOqGLFihozZowWLFigmTNn6tChQxowYID+9a9/adOmTQ+M68MPP1SlSpW0d+9eDR06VCdPnlTjxo3VunVrHThwQF999ZV+/vln6xrgrl271K9fP40aNUpHjx7VqlWr9Oyzz0qSpkyZouDgYHXv3l0XLlzQhQsXFBQU9ND8LzXuj1eS8uXLp3nz5unw4cOaMmWK5syZo0mTJtms96BcMbm4k9KpUyd98cUXmjp1qo4cOaJZs2YlGkXpP//5jyZMmKBdu3bJ2dlZXbt2tZYtXbpUr7/+ut544w39+uuvevXVV9WlSxdt3Lgxyf1dvXpVdevW1blz5/T9999r//79GjRokOLj4yU9/LrnkiVLNGnSJM2aNUvHjx/XsmXLVKFChVR/7gDSwABABgsLCzNOTk7Gw8PDZnr//feTXWfx4sUmf/781nyFChXMiBEjkuz77rvvmueff96m7ezZs0aSOXr0aIrjbNasmXnjjTes+bp165o6derY9KlevboZPHiwMcaY1atXG2dnZ3Pu3Dlr+cqVK40ks3Tp0iT3ERMTY1xdXc2cOXOSXD579mzz2GOPmatXr1ptK1asMI6OjiYiIsIYc/fzLFq0qLlz547Vp23btqZdu3bWfKVKlcyoUaOs+SFDhpiaNWta8yVKlDCLFi2y2fe7775rgoODjTHGhIeHG0nmk08+sZYfOnTISDJHjhwxxhgzd+5c4+3tbS1v2LChGT16tM02P//8cxMQEGDNSzLvvPOONX/16lUjyaxcudIYY8zGjRuNJHPlypUkPx8AAAB7uj//SRAWFmZatmxpzd+fR965c8d4eHiYjh07Wm0XLlwwkszWrVuNMWnLaR+UIyeld+/epnXr1tb866+/bho0aGDNr1692ri6ulq52P3HO3z4cJM3b14TExNjtb311ltWnhkTE2Py5MljFi9ebC2PiooyefPmNa+//nqK4wQAAEithHzs4sWLxtXV1Zw6dcqcOnXKuLm5mUuXLpmWLVuasLAwY8zd61F58uQxCxcutNa/deuWCQwMNOPGjTPG/P81qmXLlll9bt68afLmzWt++eUXm31369bNdOjQIdnYihYtalq1apVonR49eti0/fTTT8bR0dHcuHHDLFmyxHh5ednkXfeqW7duivKr+/O/pNa797NJLt6kjB8/3lStWtWaf1iumNK4jx49aiSZtWvXJrk84dysW7fOaluxYoWRZG7cuGGMMaZWrVqme/fuNuu1bdvWNG3a1Jq/9xrurFmzTL58+cxff/2V5D4fdt1zwoQJpnTp0ubWrVsPPDYA6Y93dAPIFPXr19eMGTNs2nx9fa3/X7duncaMGaPffvtNMTExunPnjm7evKnr168rb9686tevn3r27Kk1a9YoJCRErVu3VsWKFSVJ+/fv18aNG5N8N+LJkydVunTpRO1xcXEaPXq0vv76a507d063bt1SbGys8ubNa9MvYR8JAgICdPHiRUnSkSNHFBQUpMDAQGt5cHDwAz+HI0eOKDY2Vg0bNkx2eaVKleTh4WG11a5dW/Hx8Tp69Kj15He5cuXk5ORkE9fBgwet+dDQUH322WcaOnSojDH64osvNHDgQEl3nxo/efKkunXrpu7du1vr3LlzR97e3skef0BAgCTp4sWLKlOmTKLY9+/fry1bttg8wR0XF2dzHu/fpoeHh7y8vKzPFAAAIKe4N+dxcnJS/vz5bZ7qSMjrEvKgtOS0D8qRJWn69On67LPPdObMGd24cUO3bt1S5cqVreWhoaF65plndP78eQUGBmrhwoVq1qxZotfT3KtYsWLKly+fNX9vfvz777/r9u3bqlGjhrXc29tbTz75ZLLbAwAASE8FCxZUs2bNNG/ePBlj1KxZMxUoUMCmz8mTJ3X79m3Vrl3basuTJ49q1KihI0eO2PStVq2a9f8nTpzQ9evX9dxzz9n0uXXrVqKh0e9373aku7nfgQMHtHDhQqvNGKP4+HiFh4frueeeU9GiRfXEE0+ocePGaty4sfUqwAd5WP6XUvfHK0lfffWVpk6dqpMnT+rq1au6c+eOvLy8bPo8KFdMqX379snJyUl169Z9YL/krlsWKVJER44csYZcT1C7du1kR4/ct2+fqlSpYnO9+l4Pu+7Ztm1bTZ482TpfTZs2VYsWLdLlne4AHozfMgCZwsPDQyVLlkxy2alTp9S8eXP17NlT77//vnx9ffXzzz+rW7duunXrlvLmzatXXnlFjRo10ooVK7RmzRqNGTNGEyZMUN++fXX16lW1aNFCY8eOTbTthCTnfuPHj9eUKVM0efJk693g/fv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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "⭕ **Features that discourage users from using online food delivery**"
+      ],
+      "metadata": {
+        "id": "ZhTAShmnYsSL"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set the figure size\n",
+        "plt.figure(figsize=(20, 10))\n",
+        "\n",
+        "# Plot 1: Self Cooking\n",
+        "plt.subplot(3, 3, 1)\n",
+        "sns.countplot(data=data, x='Self Cooking', hue='Self Cooking', palette='Oranges', legend=False)\n",
+        "plt.title('Self Cooking')\n",
+        "\n",
+        "# Plot 2: Health Concern\n",
+        "plt.subplot(3, 3, 2)\n",
+        "sns.countplot(data=data, x='Health Concern', hue='Health Concern', palette='Oranges', legend=False)\n",
+        "plt.title('Health Concern')\n",
+        "\n",
+        "# Plot 3: Late Delivery\n",
+        "plt.subplot(3, 3, 3)\n",
+        "sns.countplot(data=data, x='Late Delivery', hue='Late Delivery', palette='Oranges', legend=False)\n",
+        "plt.title('Late Delivery')\n",
+        "\n",
+        "# Plot 4: Poor Hygiene\n",
+        "plt.subplot(3, 3, 4)\n",
+        "sns.countplot(data=data, x='Poor Hygiene', hue='Poor Hygiene', palette='Oranges', legend=False)\n",
+        "plt.title('Poor Hygiene')\n",
+        "\n",
+        "# Plot 5: Bad Past Experience\n",
+        "plt.subplot(3, 3, 5)\n",
+        "sns.countplot(data=data, x='Bad past experience', hue='Bad past experience', palette='Oranges', legend=False)\n",
+        "plt.title('Bad past experience')\n",
+        "\n",
+        "# Plot 6: Unavailability\n",
+        "plt.subplot(3, 3, 6)\n",
+        "sns.countplot(data=data, x='Unavailability', hue='Unavailability', palette='Oranges', legend=False)\n",
+        "plt.title('Unavailability')\n",
+        "\n",
+        "# Plot 7: Unaffordable\n",
+        "plt.subplot(3, 3, 7)\n",
+        "sns.countplot(data=data, x='Unaffordable', hue='Unaffordable', palette='Oranges', legend=False)\n",
+        "plt.title('Unaffordable')\n",
+        "\n",
+        "# Adjust layout to prevent overlap\n",
+        "plt.tight_layout()\n",
+        "\n",
+        "# Show the plot\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "id": "SzcGkGCrVX7b",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 406
+        },
+        "outputId": "36bf1475-d0f5-4ec8-db9a-b093668c3d69"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2000x1000 with 7 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "⭕ **Time Factor**"
+      ],
+      "metadata": {
+        "id": "42cC_qa0WxQV"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set the figure size\n",
+        "plt.figure(figsize=(20,10))\n",
+        "\n",
+        "# Plot 1: Distribution of 'Influence of time'\n",
+        "plt.subplot(2,3,1)\n",
+        "sns.histplot(data['Influence of time'], bins=30, kde=True)\n",
+        "plt.title('Influence of time')\n",
+        "plt.xticks(rotation=60)\n",
+        "\n",
+        "# Plot 2: Distribution of 'Maximum wait time'\n",
+        "plt.subplot(2,3,2)\n",
+        "sns.histplot(data['Maximum wait time'], bins=30, kde=True)\n",
+        "plt.title('Maximum wait time')\n",
+        "plt.xticks(rotation=60)\n",
+        "\n",
+        "# Plot 3: Distribution of 'Less Delivery time'\n",
+        "plt.subplot(2,3,3)\n",
+        "sns.histplot(data['Less Delivery time'], bins=30, kde=True)\n",
+        "plt.title('Less Delivery time')\n",
+        "plt.xticks(rotation=60)\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "m8_IHWeNWW6j",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 249
+        },
+        "outputId": "776a0fb7-77fa-4dad-fe13-cd6ff475e690"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2000x1000 with 3 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<h2><b>Training And Testing Models 🤖</h2>"
+      ],
+      "metadata": {
+        "id": "2Hhk6gmmCmc2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<h3><b>Problem 2 : Predict User Behavior</h3>\n",
+        "\n",
+        "*   Model 1: Deep Neural Network (DNN)\n",
+        "*   Model 2: Long Short-Term Memory (LSTM)\n",
+        "\n",
+        "*   Model 3: Gated Recurrent Unit (GRU)\n"
+      ],
+      "metadata": {
+        "id": "Gs24Mn0RnJ9l"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Ensure all entries in 'Reviews' are converted to strings\n",
+        "data['Reviews'] = data['Reviews'].astype(str)\n",
+        "\n",
+        "# Clean the 'Reviews' column by removing 'Nil' entries and stripping spaces\n",
+        "data['Reviews'] = data['Reviews'].str.strip().replace(['Nil', 'nil', 'NIL'], np.nan)\n",
+        "\n",
+        "# Drop rows with missing reviews\n",
+        "data = data.dropna(subset=['Reviews'])"
+      ],
+      "metadata": {
+        "id": "jux2EYWWqNq6"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "positive_words = ['good', 'improved', 'happy', 'love', 'satisfied', 'okay', 'best', 'no issues', 'quick']\n",
+        "\n",
+        "def label_review(review):\n",
+        "    review = review.lower()\n",
+        "    if any(word in review for word in positive_words):\n",
+        "        return 1  # Positive review\n",
+        "    else:\n",
+        "        return 0  # Negative review\n",
+        "\n",
+        "data['Sentiment'] = data['Reviews'].apply(label_review)"
+      ],
+      "metadata": {
+        "id": "J1Codx7bnMxE",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "28cacf6f-0731-4c0e-8369-84d691f265b6"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-29-1be80af244e0>:10: SettingWithCopyWarning: \n",
+            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+            "Try using .loc[row_indexer,col_indexer] = value instead\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "  data['Sentiment'] = data['Reviews'].apply(label_review)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(data['Reviews'].head())"
+      ],
+      "metadata": {
+        "id": "mBFq_J3ZvG0E",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "35da5309-09b8-4f3c-efc2-2f999df0c903"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "2     Many a times payment gateways are an issue, so...\n",
+            "11    Language barrier is also one major issue. Mosl...\n",
+            "17            Spillage, bad packaging and missing items\n",
+            "18    Once my order from kfc got exchanged with some...\n",
+            "22    I feel Swiggy has a good interface for users a...\n",
+            "Name: Reviews, dtype: object\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(data['Sentiment'].head())"
+      ],
+      "metadata": {
+        "id": "wXEbGTw2vqU_",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "af0c957b-1989-47e7-aa8f-17ce504dcb81"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "2     0\n",
+            "11    0\n",
+            "17    0\n",
+            "18    0\n",
+            "22    1\n",
+            "Name: Sentiment, dtype: int64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Model 1: Deep Neural Network (DNN)**"
+      ],
+      "metadata": {
+        "id": "is1zu2u3SzLk"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Tokenization and Padding\n",
+        "tokenizer = Tokenizer(num_words=5000, oov_token=\"<OOV>\")  # Tokenize top 5000 words\n",
+        "tokenizer.fit_on_texts(data['Reviews'])\n",
+        "sequences = tokenizer.texts_to_sequences(data['Reviews'])\n",
+        "padded_sequences = pad_sequences(sequences, maxlen=100, padding='post')\n",
+        "\n",
+        "# Train-Test Split\n",
+        "X_train, X_test, y_train, y_test = train_test_split(padded_sequences, data['Sentiment'], test_size=0.2, random_state=42)"
+      ],
+      "metadata": {
+        "id": "JTklJ-ZM7VPU"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Build the Deep Neural Network Model\n",
+        "model_dnn = Sequential([\n",
+        "    Embedding(input_dim=5000, output_dim=64, input_length=100),\n",
+        "    Bidirectional(LSTM(64, return_sequences=True)),\n",
+        "    Dropout(0.5),\n",
+        "    Bidirectional(LSTM(32)),\n",
+        "    Dense(32, activation='relu'),\n",
+        "    Dropout(0.5),\n",
+        "    Dense(1, activation='sigmoid')  # Binary classification\n",
+        "])\n",
+        "\n",
+        "# Compile the Model\n",
+        "model_dnn.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
+        "\n",
+        "# Train the Model\n",
+        "history_dnn = model_dnn.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test), batch_size=16)\n",
+        "\n",
+        "# Evaluate the Model\n",
+        "loss, accuracy = model_dnn.evaluate(X_test, y_test)\n",
+        "print(f\"Test Accuracy: {accuracy:.2f}\")\n",
+        "\n",
+        "# Predict on test data\n",
+        "y_pred = (model_dnn.predict(X_test) > 0.5).astype(\"int32\")  # Threshold at 0.5\n",
+        "\n",
+        "# Accuracy and Loss curves\n",
+        "result=pd.DataFrame(history_dnn.history)\n",
+        "fig, ax=plt.subplots(nrows=1, ncols=2,figsize=(18,6))\n",
+        "ax=ax.flatten()\n",
+        "ax[0].plot(result[['accuracy','val_accuracy']])\n",
+        "ax[0].set_title(\"Accuracy\")\n",
+        "ax[1].plot(result[['loss','val_loss']])\n",
+        "ax[1].set_title(\"Loss\")\n",
+        "\n",
+        "plt.show()\n",
+        "\n",
+        "# Classification Report\n",
+        "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))"
+      ],
+      "metadata": {
+        "id": "VpV-SBXmSto2",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 972
+        },
+        "outputId": "a3d267a2-0eae-42dc-efb5-3953c47ce551"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/10\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/embedding.py:90: UserWarning: Argument `input_length` is deprecated. Just remove it.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 359ms/step - accuracy: 0.5232 - loss: 0.6913 - val_accuracy: 0.6531 - val_loss: 0.6712\n",
+            "Epoch 2/10\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 162ms/step - accuracy: 0.6249 - loss: 0.6736 - val_accuracy: 0.6531 - val_loss: 0.6452\n",
+            "Epoch 3/10\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 160ms/step - accuracy: 0.6575 - loss: 0.6555 - val_accuracy: 0.6531 - val_loss: 0.6520\n",
+            "Epoch 4/10\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 161ms/step - accuracy: 0.7409 - loss: 0.6318 - val_accuracy: 0.7347 - val_loss: 0.5989\n",
+            "Epoch 5/10\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 186ms/step - accuracy: 0.8986 - loss: 0.5021 - val_accuracy: 0.8571 - val_loss: 0.3891\n",
+            "Epoch 6/10\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 289ms/step - accuracy: 0.9734 - loss: 0.1812 - val_accuracy: 0.8367 - val_loss: 0.4719\n",
+            "Epoch 7/10\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 162ms/step - accuracy: 0.9824 - loss: 0.1630 - val_accuracy: 0.8571 - val_loss: 0.4902\n",
+            "Epoch 8/10\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 162ms/step - accuracy: 0.9960 - loss: 0.0746 - val_accuracy: 0.8367 - val_loss: 0.6595\n",
+            "Epoch 9/10\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 164ms/step - accuracy: 0.9956 - loss: 0.0543 - val_accuracy: 0.8367 - val_loss: 0.7016\n",
+            "Epoch 10/10\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 163ms/step - accuracy: 0.9975 - loss: 0.0279 - val_accuracy: 0.8367 - val_loss: 0.6956\n",
+            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.8391 - loss: 0.6946\n",
+            "Test Accuracy: 0.84\n",
+            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 954ms/step\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1800x600 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Classification Report:\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.80      1.00      0.89        32\n",
+            "           1       1.00      0.53      0.69        17\n",
+            "\n",
+            "    accuracy                           0.84        49\n",
+            "   macro avg       0.90      0.76      0.79        49\n",
+            "weighted avg       0.87      0.84      0.82        49\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Predict sentiment of new reviews\n",
+        "new_reviews = [\"Satisfied with service\", \"Bad service and terrible taste.\"]\n",
+        "new_sequences = tokenizer.texts_to_sequences(new_reviews)\n",
+        "new_padded = pad_sequences(new_sequences, maxlen=100, padding='post')\n",
+        "predictions = model_dnn.predict(new_padded)\n",
+        "\n",
+        "print(\"Sentiment Predictions:\", [\"Positive\" if p > 0.5 else \"Negative\" for p in predictions])"
+      ],
+      "metadata": {
+        "id": "QAjRitQ578vZ",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "5bfa9e1f-c657-4d6e-a13c-fd65e52c5885"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step\n",
+            "Sentiment Predictions: ['Positive', 'Negative']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Model 2: Long Short-Term Memory (LSTM)**"
+      ],
+      "metadata": {
+        "id": "0TFXeS9oMae8"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Prepare reviews data\n",
+        "tokenizer = Tokenizer(num_words=5000)  # Keep top 5000 words\n",
+        "tokenizer.fit_on_texts(data['Reviews'].astype(str))\n",
+        "X = tokenizer.texts_to_sequences(data['Reviews'].astype(str))\n",
+        "X = pad_sequences(X, maxlen=100)  # Pad sequences to a fixed length of 100\n",
+        "\n",
+        "# Prepare target labels\n",
+        "y = data['Sentiment']\n",
+        "\n",
+        "# Train-test split\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)\n",
+        "\n",
+        "# Build the LSTM model\n",
+        "model = Sequential()\n",
+        "model.add(Embedding(input_dim=5000, output_dim=64, input_length=100))  # Embedding layer\n",
+        "model.add(SpatialDropout1D(0.2))  # Regularize with dropout on embedded features\n",
+        "model.add(LSTM(128, dropout=0.2, recurrent_dropout=0.2,return_sequences=True))  # LSTM with dropout\n",
+        "model.add(LSTM(128, dropout=0.2, recurrent_dropout=0.2,return_sequences=True))  # Second LSTM\n",
+        "model.add(LSTM(64))  # Second LSTM\n",
+        "model.add(Dense(1, activation='sigmoid'))  # Sigmoid for binary classification\n",
+        "\n",
+        "# Compile the model\n",
+        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
+        "\n",
+        "# **FORCE BUILD the model by sending a dummy input**\n",
+        "model.build(input_shape=(None, 100))  # This ensures the model initializes properly\n",
+        "\n",
+        "# Print model summary\n",
+        "model.summary()\n",
+        "\n",
+        "# Train the model\n",
+        "history_zlstm = model.fit(\n",
+        "    X_train, y_train,\n",
+        "    epochs=25,  # You can increase epochs for better results\n",
+        "    batch_size=16,\n",
+        "    validation_split=0.1,\n",
+        "    verbose=1\n",
+        ")\n",
+        "\n",
+        "# Evaluate the model on the test set\n",
+        "loss, accuracy = model.evaluate(X_test, y_test, verbose=1)\n",
+        "print(f'Test Accuracy: {accuracy:.4f}')\n",
+        "\n",
+        "# Predict on test data\n",
+        "y_pred = (model.predict(X_test) > 0.5).astype(\"int32\")  # Threshold at 0.5\n",
+        "\n",
+        "# Accuracy and Loss curves\n",
+        "result=pd.DataFrame(history_zlstm.history)\n",
+        "fig, ax=plt.subplots(nrows=1, ncols=2,figsize=(18,6))\n",
+        "ax=ax.flatten()\n",
+        "ax[0].plot(result[['accuracy','val_accuracy']])\n",
+        "ax[0].set_title(\"Accuracy\")\n",
+        "ax[1].plot(result[['loss','val_loss']])\n",
+        "ax[1].set_title(\"Loss\")\n",
+        "\n",
+        "plt.show()\n",
+        "\n",
+        "# Classification Report\n",
+        "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))\n"
+      ],
+      "metadata": {
+        "id": "H1XDkDx1y4uL",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "466f281d-fec2-4247-c59f-a49b0d015f4e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/embedding.py:90: UserWarning: Argument `input_length` is deprecated. Just remove it.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1mModel: \"sequential_7\"\u001b[0m\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_7\"</span>\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
+              "│ embedding_4 (\u001b[38;5;33mEmbedding\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │         \u001b[38;5;34m320,000\u001b[0m │\n",
+              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+              "│ spatial_dropout1d_2                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │               \u001b[38;5;34m0\u001b[0m │\n",
+              "│ (\u001b[38;5;33mSpatialDropout1D\u001b[0m)                   │                             │                 │\n",
+              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+              "│ lstm_10 (\u001b[38;5;33mLSTM\u001b[0m)                       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │          \u001b[38;5;34m98,816\u001b[0m │\n",
+              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+              "│ lstm_11 (\u001b[38;5;33mLSTM\u001b[0m)                       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │         \u001b[38;5;34m131,584\u001b[0m │\n",
+              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+              "│ lstm_12 (\u001b[38;5;33mLSTM\u001b[0m)                       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │          \u001b[38;5;34m49,408\u001b[0m │\n",
+              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+              "│ dense_10 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │              \u001b[38;5;34m65\u001b[0m │\n",
+              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
+              "│ embedding_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">320,000</span> │\n",
+              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+              "│ spatial_dropout1d_2                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SpatialDropout1D</span>)                   │                             │                 │\n",
+              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+              "│ lstm_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │          <span style=\"color: #00af00; text-decoration-color: #00af00\">98,816</span> │\n",
+              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+              "│ lstm_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │         <span style=\"color: #00af00; text-decoration-color: #00af00\">131,584</span> │\n",
+              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+              "│ lstm_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">49,408</span> │\n",
+              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+              "│ dense_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">65</span> │\n",
+              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m599,873\u001b[0m (2.29 MB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">599,873</span> (2.29 MB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m599,873\u001b[0m (2.29 MB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">599,873</span> (2.29 MB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/25\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 273ms/step - accuracy: 0.6141 - loss: 0.6776 - val_accuracy: 0.4091 - val_loss: 0.7630\n",
+            "Epoch 2/25\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 359ms/step - accuracy: 0.6151 - loss: 0.6819 - val_accuracy: 0.4091 - val_loss: 0.7158\n",
+            "Epoch 3/25\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 217ms/step - accuracy: 0.6423 - loss: 0.6570 - val_accuracy: 0.4091 - val_loss: 0.7837\n",
+            "Epoch 4/25\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 216ms/step - accuracy: 0.6724 - loss: 0.6120 - val_accuracy: 0.6364 - val_loss: 0.6518\n",
+            "Epoch 5/25\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 255ms/step - accuracy: 0.7839 - loss: 0.5681 - val_accuracy: 0.6818 - val_loss: 0.6591\n",
+            "Epoch 6/25\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 213ms/step - accuracy: 0.8723 - loss: 0.3123 - val_accuracy: 0.6818 - val_loss: 0.5115\n",
+            "Epoch 7/25\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 217ms/step - accuracy: 0.9608 - loss: 0.1604 - val_accuracy: 0.7727 - val_loss: 0.5428\n",
+            "Epoch 8/25\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 302ms/step - accuracy: 0.9786 - loss: 0.0615 - val_accuracy: 0.7727 - val_loss: 0.5275\n",
+            "Epoch 9/25\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 213ms/step - accuracy: 0.9656 - loss: 0.0778 - val_accuracy: 0.8636 - val_loss: 0.3172\n",
+            "Epoch 10/25\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 225ms/step - accuracy: 0.9922 - loss: 0.0448 - val_accuracy: 0.9091 - val_loss: 0.3901\n",
+            "Epoch 11/25\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 217ms/step - accuracy: 0.9961 - loss: 0.0327 - val_accuracy: 0.9091 - val_loss: 0.5497\n",
+            "Epoch 12/25\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 221ms/step - accuracy: 0.9985 - loss: 0.0232 - val_accuracy: 0.9091 - val_loss: 0.5471\n",
+            "Epoch 13/25\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 321ms/step - accuracy: 0.9970 - loss: 0.0153 - val_accuracy: 0.8636 - val_loss: 0.5552\n",
+            "Epoch 14/25\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 217ms/step - accuracy: 0.9854 - loss: 0.0275 - val_accuracy: 0.9091 - val_loss: 0.5589\n",
+            "Epoch 15/25\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 216ms/step - accuracy: 0.9964 - loss: 0.0130 - val_accuracy: 0.9091 - val_loss: 0.5495\n",
+            "Epoch 16/25\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 216ms/step - accuracy: 0.9975 - loss: 0.0061 - val_accuracy: 0.9091 - val_loss: 0.5625\n",
+            "Epoch 17/25\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 216ms/step - accuracy: 1.0000 - loss: 0.0078 - val_accuracy: 0.9091 - val_loss: 0.5810\n",
+            "Epoch 18/25\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 252ms/step - accuracy: 0.9956 - loss: 0.0094 - val_accuracy: 0.9091 - val_loss: 0.5915\n",
+            "Epoch 19/25\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 217ms/step - accuracy: 1.0000 - loss: 0.0052 - val_accuracy: 0.9091 - val_loss: 0.6220\n",
+            "Epoch 20/25\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 304ms/step - accuracy: 0.9906 - loss: 0.0132 - val_accuracy: 0.9091 - val_loss: 0.5484\n",
+            "Epoch 21/25\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 312ms/step - accuracy: 1.0000 - loss: 0.0113 - val_accuracy: 0.9091 - val_loss: 0.5268\n",
+            "Epoch 22/25\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 236ms/step - accuracy: 0.9949 - loss: 0.0242 - val_accuracy: 0.9091 - val_loss: 0.3066\n",
+            "Epoch 23/25\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 236ms/step - accuracy: 1.0000 - loss: 0.0112 - val_accuracy: 0.9091 - val_loss: 0.5300\n",
+            "Epoch 24/25\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 235ms/step - accuracy: 0.9843 - loss: 0.0210 - val_accuracy: 0.9091 - val_loss: 0.4969\n",
+            "Epoch 25/25\n",
+            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 240ms/step - accuracy: 0.9854 - loss: 0.0254 - val_accuracy: 0.9091 - val_loss: 0.3999\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - accuracy: 0.9200 - loss: 0.2155\n",
+            "Test Accuracy: 0.9200\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 659ms/step\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1800x600 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Classification Report:\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.94      0.94      0.94        16\n",
+            "           1       0.89      0.89      0.89         9\n",
+            "\n",
+            "    accuracy                           0.92        25\n",
+            "   macro avg       0.91      0.91      0.91        25\n",
+            "weighted avg       0.92      0.92      0.92        25\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Predict sentiment of new reviews\n",
+        "new_reviews = [\"Bad packaging and missing items\", \"Was satisfied by my order.\"]\n",
+        "new_sequences = tokenizer.texts_to_sequences(new_reviews)\n",
+        "new_padded = pad_sequences(new_sequences, maxlen=100, padding='post')\n",
+        "predictions = model.predict(new_padded)\n",
+        "\n",
+        "print(\"Sentiment Predictions:\", [\"Positive\" if p > 0.5 else \"Negative\" for p in predictions])"
+      ],
+      "metadata": {
+        "id": "29c1EHaJNJ8x",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "bcaca185-2994-448d-84b2-d4c9c8e13c93"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 227ms/step\n",
+            "Sentiment Predictions: ['Negative', 'Negative']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Model 3: Gated Recurrent Unit (GRU)**"
+      ],
+      "metadata": {
+        "id": "-pJRGOHUTRL6"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create and fit the tokenizer\n",
+        "tokenizer = Tokenizer(num_words=5000)  # Keep top 5000 words\n",
+        "tokenizer.fit_on_texts(data['Reviews'].astype(str))\n",
+        "\n",
+        "# Get the word_index from the tokenizer\n",
+        "word_index = tokenizer.word_index\n",
+        "\n",
+        "# Build GRU model\n",
+        "model = Sequential()\n",
+        "model.add(Embedding(input_dim=len(word_index) + 1, output_dim=128, input_length=100))\n",
+        "model.add(GRU(units=128, return_sequences=False))\n",
+        "model.add(Dropout(0.5))\n",
+        "model.add(Dense(1, activation='sigmoid'))\n",
+        "\n",
+        "# Compile the model\n",
+        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
+        "\n",
+        "# Train the model\n",
+        "model.fit(X_train, y_train, batch_size=16, epochs=25, validation_data=(X_test, y_test))\n",
+        "\n",
+        "# Evaluate the model on the test set\n",
+        "loss, accuracy = model.evaluate(X_test, y_test, verbose=1)\n",
+        "print(f'Test Accuracy: {accuracy:.4f}')\n",
+        "\n",
+        "# Predict on test data\n",
+        "y_pred = (model.predict(X_test) > 0.5).astype(\"int32\")  # Threshold at 0.5\n",
+        "\n",
+        "# Accuracy and Loss curves\n",
+        "result=pd.DataFrame(history_dnn.history)\n",
+        "fig, ax=plt.subplots(nrows=1, ncols=2,figsize=(18,6))\n",
+        "ax=ax.flatten()\n",
+        "ax[0].plot(result[['accuracy','val_accuracy']])\n",
+        "ax[0].set_title(\"Accuracy\")\n",
+        "ax[1].plot(result[['loss','val_loss']])\n",
+        "ax[1].set_title(\"Loss\")\n",
+        "\n",
+        "plt.show()\n",
+        "\n",
+        "# Classification Report\n",
+        "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))"
+      ],
+      "metadata": {
+        "id": "KYluq4ZD67bO",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "b43ae704-1111-413b-b4aa-a7da776a9060"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/25\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/embedding.py:90: UserWarning: Argument `input_length` is deprecated. Just remove it.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 120ms/step - accuracy: 0.6104 - loss: 0.6840 - val_accuracy: 0.6400 - val_loss: 0.6429\n",
+            "Epoch 2/25\n",
+            "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 92ms/step - accuracy: 0.6184 - loss: 0.6245 - val_accuracy: 0.6400 - val_loss: 0.5760\n",
+            "Epoch 3/25\n",
+            "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 97ms/step - accuracy: 0.7124 - loss: 0.5082 - val_accuracy: 0.9200 - val_loss: 0.4254\n",
+            "Epoch 4/25\n",
+            "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 146ms/step - accuracy: 0.9247 - loss: 0.2955 - val_accuracy: 0.9200 - val_loss: 0.2687\n",
+            "Epoch 5/25\n",
+            "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 153ms/step - accuracy: 0.9720 - loss: 0.1317 - val_accuracy: 0.8400 - val_loss: 0.3963\n",
+            "Epoch 6/25\n",
+            "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 92ms/step - accuracy: 0.9987 - loss: 0.0253 - val_accuracy: 0.9200 - val_loss: 0.1983\n",
+            "Epoch 7/25\n",
+            "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 91ms/step - accuracy: 0.9994 - loss: 0.0191 - val_accuracy: 0.9200 - val_loss: 0.2451\n",
+            "Epoch 8/25\n",
+            "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 95ms/step - accuracy: 1.0000 - loss: 0.0137 - val_accuracy: 0.8800 - val_loss: 0.3076\n",
+            "Epoch 9/25\n",
+            "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 92ms/step - accuracy: 0.9924 - loss: 0.0095 - val_accuracy: 0.9200 - val_loss: 0.2994\n",
+            "Epoch 10/25\n",
+            "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 91ms/step - accuracy: 1.0000 - loss: 0.0027 - val_accuracy: 0.9200 - val_loss: 0.2769\n",
+            "Epoch 11/25\n",
+            "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 105ms/step - accuracy: 1.0000 - loss: 0.0015 - val_accuracy: 0.9200 - val_loss: 0.2629\n",
+            "Epoch 12/25\n",
+            "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 146ms/step - accuracy: 1.0000 - loss: 0.0015 - val_accuracy: 0.9200 - val_loss: 0.2642\n",
+            "Epoch 13/25\n",
+            "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 97ms/step - accuracy: 1.0000 - loss: 0.0014 - val_accuracy: 0.8800 - val_loss: 0.2609\n",
+            "Epoch 14/25\n",
+            "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 95ms/step - accuracy: 1.0000 - loss: 0.0011 - val_accuracy: 0.8800 - val_loss: 0.2544\n",
+            "Epoch 15/25\n",
+            "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 93ms/step - accuracy: 1.0000 - loss: 0.0012 - val_accuracy: 0.8800 - val_loss: 0.2636\n",
+            "Epoch 16/25\n",
+            "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 93ms/step - accuracy: 1.0000 - loss: 5.7661e-04 - val_accuracy: 0.8800 - val_loss: 0.2744\n",
+            "Epoch 17/25\n",
+            "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 93ms/step - accuracy: 1.0000 - loss: 5.1287e-04 - val_accuracy: 0.8800 - val_loss: 0.2786\n",
+            "Epoch 18/25\n",
+            "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 159ms/step - accuracy: 1.0000 - loss: 5.5192e-04 - val_accuracy: 0.8800 - val_loss: 0.2855\n",
+            "Epoch 19/25\n",
+            "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 154ms/step - accuracy: 1.0000 - loss: 6.2982e-04 - val_accuracy: 0.8800 - val_loss: 0.2929\n",
+            "Epoch 20/25\n",
+            "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 92ms/step - accuracy: 1.0000 - loss: 6.3369e-04 - val_accuracy: 0.8800 - val_loss: 0.3039\n",
+            "Epoch 21/25\n",
+            "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 94ms/step - accuracy: 1.0000 - loss: 5.0888e-04 - val_accuracy: 0.8800 - val_loss: 0.3110\n",
+            "Epoch 22/25\n",
+            "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 92ms/step - accuracy: 1.0000 - loss: 4.4154e-04 - val_accuracy: 0.8800 - val_loss: 0.3187\n",
+            "Epoch 23/25\n",
+            "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 94ms/step - accuracy: 1.0000 - loss: 5.7314e-04 - val_accuracy: 0.8800 - val_loss: 0.3230\n",
+            "Epoch 24/25\n",
+            "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 95ms/step - accuracy: 1.0000 - loss: 4.3548e-04 - val_accuracy: 0.8800 - val_loss: 0.3277\n",
+            "Epoch 25/25\n",
+            "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 97ms/step - accuracy: 1.0000 - loss: 5.7217e-04 - val_accuracy: 0.8800 - val_loss: 0.3350\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - accuracy: 0.8800 - loss: 0.3350\n",
+            "Test Accuracy: 0.8800\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 331ms/step\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1800x600 with 2 Axes>"
+            ],
+            "image/png": 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evTvr1q0777EDBgzAZDKd87j88sursGIRERERqckqPZju2bMnMTExxfYtXLiQnj17VvZLi4iIiIhIKcyZM4cJEybw/PPPs2nTJtq3b8+wYcM4fvx4icd///33HDt2rPCxfft2LBYLI0aMqOLKRURERKSmcivrCadPnyY2Nrbw+wMHDrBlyxaCg4Np3LgxEydO5MiRI3z22WcA3HPPPbz77rs88cQT3HnnnSxevJivv/6auXPnOu5diIiIiPwDu91Oek4+yRm5JGfmkZyZW7idkpnLqYxcUgr25+bbnF2u080a1x1PN4uzy5AqNHnyZMaNG8eYMWMAmDZtGnPnzmXmzJk8+eST5xwfHBxc7PvZs2fj4+OjYFpERESqD5sVYhfBllmQnQqeAeAVAJ6BxlevwLP2nf21YL+bh7PfQa1X5mB6w4YNDBw4sPD7CRMmAHD77bfzySefcOzYMeLi4gqfb9q0KXPnzuWRRx5hypQpNGrUiI8++ohhw4Y5oHwRERFxNVabnZTMolA5OTOvIGQuvp2SmcepzFxSCrbzbXZnl15j2PVP5VJyc3PZuHEjEydOLNxnNpsZMmQIq1evLtUYM2bMYNSoUfj6+p73mJycHHJycgq/T0tLK3/RIiIiIueTkQSbP4cNMyEl7sLHn4+bdwmh9d/Ca6/A8xxTsN/i7rj3VQuVOZgeMGAA9n/4beWTTz4p8ZzNmzeX9aVERESklsvJtxbOVE7OKPh6JlQ+K2A+e4ZzWnZeuYNTb3cLdXzcCfLxoI6vO3V8PAoe7tTxNba93Ktlb+gq5W7Rv4ErSUpKwmq1Eh4eXmx/eHg4u3btuuD569atY/v27cyYMeMfj5s0aRIvvvhihWoVERERKZHdDofXw/qPYMcPYM019nsFQcdbod7FkJNmzJw+8zU7rWD7b19zTxvn5mfB6Sw4nXjel70gd5/zBNtnBdwXmrltKXN8W2PU3ncmIiIiVcZut5OZay0WJJ+9PEaxJTQKQuiUzFwycq3lfk1/L7dzQuUgn4Kw2bdg/5nguSCE9nLX8hQijjZjxgzatWtHt27d/vG4iRMnFn7aEowZ0xEREZVdnoiIiNRmuRmw7RsjkE7YVrS/QSfoOhbaXgfu3mUb02a9QHh99v7zHJOXYYyVl2k8TieU/z26+/5DsH1mdvb5Zm4HVOtwu3pWJSIiIk5js9lJz84vCpHPms189vIYZ89wTs7MK/fazGYTBJ0dKv8tbC6c4ezjTrCvR+GxmtUr4hghISFYLBYSE4vPBkpMTKRevXr/eG5GRgazZ8/mpZdeuuDreHp64unpWaFaRURERAA4sQc2zIAtXxphMICbF7S9AbreCQ07l39sswW86xiP8rLmG3VVJODOyzTGysswHunHyl+Pdx3498Hyn19JFEyLiIgAp3Py+Xp9PF+sPcSJ9JwLn1Bb2SEzz4q1nOsxe1jMZ81adi+YxVw8VP576Bzg5Y7ZbHLwGxGR0vLw8KBz587ExMRwzTXXAGCz2YiJieH+++//x3O/+eYbcnJyuPXWW6ugUhEREXFp1jzYNdeYHX1wedH+4Cjochd0uBl8gs9/flWyuBm1VKQeax7kpF9g+ZHUc78/+5j8LGMsU/Wc1KNgWkREXFpiWjafrDrIrDWHSMvOd3Y51YqPh6VweYxiofJZ4XKQjwfBZ2Y7+3rg62HBZFLILFLTTJgwgdtvv50uXbrQrVs33nrrLTIyMhgzZgwAo0ePpmHDhkyaNKnYeTNmzOCaa66hbt26zihbREREXEHaUdj4KWz8pGhJDJMZWlwGXe+CqIFgrp7Ba4VY3CsebufnGuH2maVFqhkF0yIi4pJ2JaQxfdkBfv7zCHlWY3Zw0xBfxvZtSo+ourhytOrj4UaQj7vWYxZxISNHjuTEiRM899xzJCQk0KFDB+bNm1fYEDEuLg7z337h2717NytWrGDBggXOKFlERERqM7sdDiwzZkfvmgv2gt40vmHQ+XbodDsEqVfFBbl5gFtdoHpOIjDZ7eXta1910tLSCAwMJDU1lYCAAGeXIyIiNZTdbmdl7Ek+XL6fZXtOFO7vFhnMuH5RDG4VpiUlRKqY7vNcV1Vf+7lbj7HuwEka1fGhUR1vIoKNr4He7vqkh4iISHWRlQJ/zjbWj07aU7S/SW9jdnSrK42wVaq10t7naca0iIjUern5Nn7depQPl+1nV0I6YDTcu6xtfcb2bUrHxhVoaiEiIjXCitgTfLUu/pz9fp5uNKrjXfDwKfY1oo4PgT7uTqhWRETExRzbasyO3vZNUdM/Dz9oP8pYPzq8jXPrk0qhYFpERGqt1Kw8vloXx8crD5CYZjQ09PGwcGOXCO7q05SIYB8nVygiIlVlaJt6BPl4cDg5i8PJmRxOzuJEeg6nc/LZlZBe+IfLv/P3cjsrsC4eWjcK9ibAS8G1iIhIueRlw18/GYH04XVF+8PaGLOjLx4Jnv7Oq08qnYJpERGpdQ4nZ/LxyoPMXhdHRq6xFlmovyd39Irklu6NCfLRR79ERFzNwFZhDGwVVmxfdp61WFB9Zjs+OYsjyZkknc4lPTufncfS2HksrcRxA84Krs8sD3J2kO2v4FpERKS45IOwYSZs+hyyThn7zO7Q5iroOhYa9wQts+USFEyLiEitse1wKh8u389v245htRktFFqE+zGubxRXdWiAp5ua+YmISBEvdwvNwvxoFuZX4vNZuVaOpBhB9eHkLA6fyiwWZJ/MyCUtO5+/jqXx13mC6yAfdyOkDio+6zoi2IeGdbzx89SvZCIi4gJsVohdZMyO3rsQKGh5F9AIutwBHUeDf7gzKxQn0F2QiIjUaDabnSV7jvPhsv2s2X+qcH/vZnUZ1zeK/i1C1dRKRETKxdvDQrMwf5qFlfwx4szc/JJnXJ8yviZn5pFS8Nh+pOTguo6PewkzrotmXft46Fc2ERGpwTKSYPPnxgzplLii/dGDjeU6mg8Di/5b56p05UVEpEbKzrPy4+YjfLTiALHHTwPgZjZxZfsGjO3blIsaBDq5QhERqe18PNxoEe5Pi/CSg+vTOfkcOSu4jj8z4zrF+JqSmUdyZh7JmalsO5Ja4hh1fT3OWR7EmHHtTcMgH7w99GkgERGpZux2OLzemB294wew5hr7vYKg463Q5U6oG+3UEqV6UDAtIiI1SnJGLl+sOcSnqw+SdNq4wfHzdOPm7o25o1ckDYK8nVyhiIiIwc/TjZb1/GlZr+TgOj07r9hM67/PuE7LzudkRi4nM3L583DJwXWInwcNz27I+LcZ117uCq5FRKSK5GbAtm+MQDphW9H+Bp2MtaPbXgfu+n1NiiiYFhGRGuFgUgYzVhzgm43xZOfZAGgQ6MWdfZoysmuEmkuJiEiN4+/lTuv67rSuH1Di86lZeYUzruP/vmTIqUzSc/JJOp1L0ulc/oxPKXGMUH/PwqC6QaAXnm5mTCYTZpMJixlMJhMWswmzCcyF+wu+N5sK9hV/zmSi4Jii5898f77nSnodsxksphKeM5uwmEqooWDbcs7roGW7RESc6cQe2DADtnwJOQVLV7l5QdsboOud0LCzc+uTakvBtIiIVGsbDyUzfdl+5v+VgL2gP8ZFDQK4u18Uw9vVx91idm6BIiIilSTQ251Ab3faNDh/cB3/t4aMRbOuM8nItXIiPYcT6Tlsjkup2uKrmKkg1C4ptD4TdHu6mbmsXX3+1T+KMH8vZ5csIlKzWfNg11xjdvTB5UX7g6Ogy13Q4WbwCXZefVIjKJgWEZFqx2qzs/CvBKYvP8DGQ8mF+we2DGVc3yh6RtfVzCgREXF5gd7uBDYMpG3Dc/sq2O12UrPyioXWx1KzybfasNrt2OxGA2Hb37atdox9Z763GWOdec5ut2M9c56Ngv3GGEXPnT2eHbudwnPO3j7z2kXjFZz7t+dKw24Hq92OlX8+fsaKA3yx5hC3dG/CPf2jCAtQQC0iUiZpR2Hjp7DxEzidYOwzmaHFZUYzw6iBYNbkISkdBdMiIlJtZOVa+XZjPB+tOMChk5kAeFjMXNOxAWP7Rp23uZSIiMgFbf0G/voRmvYzHqGtjGm2tZTJZCLIx4MgH48Sg+uapFjg/bfQuqTnjP0Ubp95Lv5UJlP/iGVTXAozVx5g1loF1CIipWK3w4FlxuzoXXPBbjX2+4ZB59uh0+0QFOHcGqVGUjAtIiJOdyI9h89WH+TzNYdIycwDjFlgt/VowuheTfRxWxERqbg9v8OuX40HgG8oRPYxQurIflA3ulYH1TWZyWTCzVLxa9Mi3J9BrcJYEZvEW4v2svFQcmFAfXP3xozvH62AWkTkbFkp8OdsY/3opD1F+5v0NmZHt7oS3DycVp7UfCa73V66z0Y5UVpaGoGBgaSmphIQUPL6aiIiUvPEHk/no+UH+H7zEXLzjYaGjYN9uKtPU0Z0aYSPh/5+KlLb6T7PdVX5tT+6BfbFwIHlELcG8rOKP+/fAJr2LQiq+0KdJpVfkziN3W5nZexJ3lq0hw0Fy4Z5uJm5uVtjxg+IJlwBtYi4smNbjdnR276BPOOTrHj4QftRxvrR4W2cW59Ue6W9z1MwLSIiVcput7Nm/ymmL9/P4l3HC/d3iAjiX/2iGHpRPSxmzVgTcRW6z3NdTr32+TlwZKPxseQDy+HwOrDmFj8mqElBUN3fCKoD6ldtjVIl7HY7q/YZAfX6g8UD6nv6R1MvUAG1iLiIvGz46ycjkD68rmh/WBtjdvTFI8FTSytK6SiYFhGRaiXfauO37QlMX7afbUdSAeMT05e0DufuflF0blJHDQ1FXJDu81xXtbr2eVkQv9YIqQ8sM0LrM+tnnlG3efEZ1b4hzqlVKoXdbmf1vpO8tWgv6w6eAoyA+qauEYwf0EwBtYjUXskHYcNM2PQ5ZBk//zC7Q5uroOtYaNxTS11JmSmYFhGRauF0Tj6z18Xx8cqDHEkxPjbt6WZmRJdG3NUniqYhvk6uUEScSfd5rqtaX/ucdGO5jwPLjMexP4G//doUdpERVEf2hcje4F3HKaWKY9ntdlbvP8lbC88KqC1mRnWLYPyAaOoHeju5QhERB7BZIXaRMTt670IK/xsX0Ai63AEdR4N/uDMrlBpOwbSIiDhVQmo2H686wJdr40jPzgegrq8Ho3tGcmuPxtT183RyhSJSHeg+z3XVqGuflQyHVhXNqD6+428HmKD+xUWNFJv01Meda7jCgHrRXtYdKAqoR3Y1AuoGQQqoRaQGykiCzZ8bM6RT4or2Rw82lutoPgws6vMjFadgWkREnOKvo2l8tHw/P/95lHyb8Z+YqFBfxvWN4tqODfFytzi5QhGpTnSf57pq9LXPSIKDy4uC6pN7iz9vskDDTkXLfkR0Bw8f59QqFba6YA3qtWcF1Dd2bcS9A5opoBaRmuHIJlg7DXb8UNRTwSsIOt4KXe6EutFOLU9qHwXTIiJSZex2O8v3JjF9+X6W700q3N+taTB3941iUKswzGpoKCIl0H2e66pV1z7tWEFQvcz4mnyw+PMWD2jUtSiobtQF3PTJoZpm9b6TTInZw5r9RkDtbjFxY5cI7h3YjIYKqEWkujq4Aj69qqh3QoNOxtrRba8Dd/3sksqhYFpERCpdbr6Nn/88ykfL97MrIR0AswmGt6vPuL5RtI8Icm6BIlLt6T7PddXqa58SVzSb+sAySD9a/Hk3b2jcvWjpjwYd9dHpGmTN/pNMWbSX1ftPAkZAPaJLBPcOiKZRHc2MF5FqxJoH0/rAiV3Gch2DnoaGnZ1dlbgABdMiIlJpUjPz+HJdHJ+sOkBiWg4APh4WRnaN4M7eTYkI1i9lIlI6us9zXS5z7e12OLW/KKQ+uBwyThQ/xsMPmvQyZlM37Qf12oFZS19Vd2v3n2RKzF5W7VNALSLV1Kp3YMEz4FMXHtioRr1SZRRMi4iIw8WfymTmygPMWR9PZq7xUbAwf0/G9G7Kzd0aE+jj7uQKRaSm0X2e63LZa2+3w4ndBSH1MmNmdXZK8WO8Ao2Q+kxQHdYaTFoSq7pad+AUU2L2sDK2KKC+oXME9w1UQC0iTpR2FN7tCrmn4eqpxnrSIlVEwbSIiDjMn/EpTF++n9+2HaOgnyGt6vkztm8UV7VvgIeb2bkFikiNpfs816VrX8Bmg8TtRbOpD66E3PTix/iEQNMzQXV/o0mVgupz2ayQnWoE/Vkp53615kKrK6Be20p5+fUHTzFl0V5WxBr9NtzMJkZ0MZok6tNkIlLlvr0Ttn9n9Di4cwGY9TubVB0F0yIiUiE2m53Fu47z4fL9rCvoQg/Qt3kI4/pG0bd5CCb9UiwiFaT7PNela38e1nw49iccWGoE1YdWQ35W8WP86xc1UmzaD+o0cU6tleFMuJyVfP6AucSvqZCTWooXMBkNvwY8BSHNKuUtlBRQ39C5EfcNVEAtIlXkwDL49EowmWHcH9Cgg7MrEhejYFpERMolO8/KD5uPMH35fvafyACMX6iuat+AsX2jaNNAP4dFxHF0n+e6dO1LKT8XjmwsmlEdv9aY+Xu2oMZGE8Wm/YyZ1QENnFPrGdb8v81cTi5dsJydAjlpFX99d1/wDgKvoOJfs5Jh92/GMSYLdLwF+v8bAhtV/DVLsOHgKabE7GX53qKA+vpOjbh/kAJqEalE1jx4vzck7Yau4+DyN5xdkbggBdMiIlJmm+OS+dfnGzmebjQ09Pd04+YejbmjVyT1A72dXJ2I1Ea6z3NduvbllJcF8euMkPrAMiO0tuUXP6Zus6LZ1JF9wS+07K9TLFw+O1guaSZzavHv/74USXl4+J0bLJfmq1cguHmcf9xjW+GPV2HPPON7iwd0uQv6Plq+f6dS2HjoFG8tKh5QX9epIfcPbE7jugqoRcTBVr4NC581loF6YIMaHopTKJgWEZEy+WP3ce79YhNZeVYaBHpxZ5+mjOwagb+XGhqKSOXRfZ7r0rV3kJzTELemoJHiMmMZELut+DFhbYyAunF3o/lidQyXveuctS8QLJV8/xG3Fha/bAT8YMyy7nEP9Hqg0kKcjYeSmRKzl2V7TgBgMZu4rmND7h/UjCZ1fSvlNUXExaQeMRoe5mWo4aE4lYJpEREpte82HuaJ77Zitdnp1yKU92/phK+nm7PLEhEXoPs816VrX0myUuDQqqIZ1YnbKzaeh//fguTAEoLlOiUEzlUQLleU3Q77l0DMS3B0k7HPKxB6PwTd7wGPygmLN8UlM2XRXpaeFVBf27Eh9w9sRmSIAmoRqYBvxsCO76FRN7hzvhoeitMomBYRkQuy2+18uGw/k37fBcC1HRvy3+svxsNNNzAiUjV0n+e6dO2rSMZJI6Q+uNyYTe3mdYEZzHWKL4thcYE/VNvtsGsuLH4FTuw09vmGQt/HoMsYcPOslJfdHGfMoF6yuyigvqZDQx4YpIBaRMph/1L47Cqj4eHdS6B+e2dXJC5MwbSIiPwjm83Oq7/tZMaKAwCM69uUiZe1xmw2ObkyEXElus9zXbr2Uu3YrLD9O2MN6uSDxr7ACKNBYvubKi2k3xKfwpRFe/hDAbWIlFd+LkzrYzQ87HY3DH/d2RWJi1MwLSIi55Wbb+Pxb//kpy1HAXhqeCvu7hft5KpExBXpPs916dpLtWXNg81fwNLXIN24V6JuMxj4FLS5ttI+Gv/3gNpsgms6NuSBQc1pqoBaRP7Jyimw8LmChocbjU++iDiRgmkRESnR6Zx8xn+xkeV7k3Azm3jthou5rlMjZ5clIi5K93muS9deqr28LFg/A1ZMhsyTxr7wdjDoGWgxDEyV8ymzP+NTmBKzl8W7jgMFAXUHo0liVKhfpbymiNRgxRoevgcdb3F2RSIKpkVE5FxJp3O485P1bD2cire7hfdv7cSAlmHOLktEXJju81yXrr3UGDnpsOZ9WPUO5KQZ+xp1g8HPQdO+lfayWw+nMGXRXmLOCqivLgiooxVQi8gZ39wBO36AiO4wZp4aHkq1oGBaRESKiT+VyW0z1nLwZCZ1fNyZeUdXOjau4+yyRMTF6T7PdenaS42TeQpWvgVrP4T8LGNf1AAY9Bw06lxpL7v1cApvx+xl0c6igPqq9g24f1BzmoUpoBZxafuXwGdXFzQ8XAr1L3Z2RSKAgmkRETnLjqOp3PHxek6k59AwyJvP7uqmmTYiUi3oPs916dpLjZWeAMvegI2fgC3P2NfqChj4NIS3qbSX3XY4lSkxe1m0MxEwVhK5qn0DHlBALeKa8nNhWm9I2gPd/gXDX3N2RSKFFEyLiAgAq/ed5O7PNpCek0+rev58emc3wgO8nF2WiAig+zxXpmsvNV7yIVj6X/jzK7DbABO0GwEDnoS6lddUevsRI6Be+FdRQH3lxQ14cHAzmoX5V9rrikg1s+ItWPQ8+IbC/RvU8FCqFQXTIiLCb9uO8fDsLeRabXRrGsz00V0I9HZ3dlkiIoV0n+e6dO2l1jixG/74D/z1o/G92Q063gr9noDAhpX2stuPpPJ2zF4WnBVQX3FxAx4c1Izm4QqoRWq11MPwbjej4eE170OHm51dkUgxCqZFRFzc56sP8tzPO7DbYdhF4UwZ1REvd4uzyxIRKUb3ea5L115qnaNbYPErELvQ+N7iCd3GQZ9HwDek0l62pID68nb1eXBwc1oooBapnb6+3fhjWEQPGPO7Gh5KtaNgWkTERdntdv63cA9vL44F4ObujXn56rZYzCYnVyYici7d57kuXXuptQ6thpiXIG6V8b2HH/S4F3rdD16BlfayO44aAfX8HQqoRWq1fX/A59cYDQ//tQzqtXN2RSLnUDAtIuKC8q02nv1pO1+tiwfg4SHNeWhwc0wmhdIiUj3pPs916dpLrWa3w74YiHkZjm0x9nkFQZ+HjSZlHj6V9tJ/HU3j7Zi9zNuRABgB9fB29XlwUHNa1lNALVKj5efC+73g5F7ofg9c9l9nVyRSotLe52muv4hILZGdZ2X8rE18tS4eswleuaYtDw9poVBaREREpKqZTNBsCNy9BG78HEJaQnYKLHoB3u4A66YbAVMlaNMggGm3deb3h/pyWdt62O0wd+sxhr21jE9WHqiU1xSRKrJmqhFK+4bCgInOrkakwhRMi4jUAqmZedw2Yy0L/0rEw83Me7d04tYeTZxdloiIiIhrM5mgzVVw72q4ZhoENYHTifDbY/BuZ9g8C6z5lfLSresH8P6tRkA97KJwACb9vov4U5mV8noiUslSD8PS14ztS14G7yCnliPiCAqmRURquITUbG78YDXrDybj7+nGZ3d249K29Z1dloiIiIicYbZAh5vg/g1w+ZvgVw9S4uCne+H9nrDjB7DZKuWlW9cPYNqtnekZVZecfBsv/vJXpbyOiFSy+U9DXiY07gntRzm7GhGHUDAtIlKDxR4/zfXvr2J3Yjph/p58fU9PekTVdXZZIiIiIlISNw/oOhYe3Fww47EOJO2Bb+6AD/vD3oXG+tQOZjKZeOnqi3Azm1i0M5GYnYkOfw0RqUT7FsNfP4LJAsPfMD6NIVILKJgWEamhNsUlc8O0VRxJySIqxJfvxveidX01jxIRERGp9jx8oPeD8NBW6P8kePhDwlaYdQN8fBkcXOnwl2we7s9dfZoC8MIvO8jOszr8NUSkEuTnwG+PG9vd7oZ6bZ1bj4gDKZgWEamB/th1nJunryElM4/2EUF8c09PIoIrr7u7iIiIiFQCrwAYOBEe+hN6PQBuXhC3Gj4ZDp9fB0c2OfTlHhjcnHoBXsSfymLa0n0OHVtEKsnqqXAyFnzDjJ8XIrWIgmkRkRrm242HGfvZBrLzbPRvEcqXY7tT18/T2WWJiIiISHn51oWhrxhLfHS5C8xusC8Gpg+EObfC8V0OeRk/TzeeuaI1AO8t2cehkxkOGVdEKklKPCx73dge+jJ4BTq3HhEHUzAtIlJD2O12pi3dx2Pf/InVZue6jg356PYu+Hq6Obs0EREREXGEgAZwxWSjSeLFowAT7PwF3usB3/8LTh2o8Etc3q4+fZqFkJtv44Wfd2CvhDWtRcRBFpzV8PDikc6uRsThFEyLiNQANpudl3/dyf/9bsyW+Ve/KN4Y0R53i36Mi4iIiNQ6wU3hug/g3tXQ+krADltnw7td4NcJkHas3EObTCZeuOoi3C0m/th9gkU7jzuubhFxnNgY+OsnNTyUWk2JhohINZebb+PhOVuYudKYIfPM5a2ZOLw1ZrNuTERERERqtbDWMPILGLcYogeBLR82zIC3O8CCZyDjZLmGbRbmx9i+UQC88PMOsnLVCFGkWsnPgd+fMLa7/0sND6XWUjAtIlKNnc7J565P1/Pzn0dxM5t4a2SHwl8iRERERMRFNOwMt/0Ad8yFiB6Qnw2r3oEp7eGPSZCdVuYhHxjUjAaBXhxJyeL9JbGVULSIlNvqd4saHg540tnViFQaBdMiItVU0ukcbvpwDcv3JuHjYWHGHV25pmNDZ5clIiIiIs4S2QfunAe3fAv1LobcdFj6f0ZAvfJtyMsq9VA+Hm48e0UbAKYt3c+BJDVCFKkWUuJh2RvG9tBX1PBQajUF0yIi1VDcyUxueH8V246kEuzrwVfjetC/RaizyxIRkVps6tSpREZG4uXlRffu3Vm3bt0/Hp+SksJ9991H/fr18fT0pEWLFvz2229VVK2ICzOZoPklcPdSGPEJ1G0OWadg4bPwdkdY/xHk55ZqqEvb1qNfi1ByrTaeVyNEkeph/lMFDQ97wcU3OrsakUqlYFpEpJrZcTSV695fxcGTmTSq48239/SkfUSQs8sSEZFabM6cOUyYMIHnn3+eTZs20b59e4YNG8bx4yU3RcvNzeWSSy7h4MGDfPvtt+zevZvp06fTsKE+2SNSZcxmuOhauHcNXP0eBDaG9GMw91GjSeKWr8D2z2tHm0wmXrzqIjwsZpbtOcH8HYlVVLyIlCh2Eez82Wh4eLkaHkrtp2BaRKQaWbUviZEfrCHpdA6t6vnz/fheRIX6ObssERGp5SZPnsy4ceMYM2YMbdq0Ydq0afj4+DBz5swSj585cyanTp3ixx9/pHfv3kRGRtK/f3/at29fxZWLCBY36HgLPLABLnvdWJM25RD8eA+83wv++hn+YSZ00xBf7u5n9DB5+de/yMzNr6rKReRs+Tnw21kND8Mvcm49IlVAwbSISDUxd+sx7pi5ntM5+XRvGszX9/QkLMDL2WWJiEgtl5uby8aNGxkyZEjhPrPZzJAhQ1i9enWJ5/z888/07NmT++67j/DwcNq2bct//vMfrNbzz87MyckhLS2t2ENEHMjNE7rfDQ9tgSEvgFcQnNgFX98GHw4wZmKeJ6C+b2AzGgZ5cyQli3cXqxGiiFOsegdO7QO/cDU8FJehYFpEpBr4bPVB7v9qE7lWG5e1rcend3YjwMvd2WWJiIgLSEpKwmq1Eh4eXmx/eHg4CQkJJZ6zf/9+vv32W6xWK7/99hvPPvssb775Jq+88sp5X2fSpEkEBgYWPiIiIhz6PkSkgIcv9HkEHvoT+j0B7r5wbAt8cT0sf6PEU7w9LDx/pdEIcfry/ew7cboKCxYRNTwUV6VgWkTEiex2O28u2M1zP+3AbodbezTm3Zs74eVucXZpIiIi52Wz2QgLC+PDDz+kc+fOjBw5kqeffppp06ad95yJEyeSmppa+IiPj6/CikVckHcQDHraCKi7jjP2Lf8fZJ4q8fBL2oQzsGUoeVY7L6gRokjVmj8R8rOgSW9oN8LZ1YhUGQXTIiJOkm+1MfH7bbxT8HHJCZe04OWr22Ixq8GFiIhUnZCQECwWC4mJxZueJSYmUq9evRLPqV+/Pi1atMBiKfpDauvWrUlISCA3N7fEczw9PQkICCj2EJEq4BcKw1+Heu0gLwPWvF/iYSaTiReuuggPNzPL9ybx+/aSPzEhIg62dxHs/MVoeDhcDQ/FtSiYFhFxgqxcK/d8sYnZ6+Mxm+A/17bjwcHNMekmREREqpiHhwedO3cmJiamcJ/NZiMmJoaePXuWeE7v3r2JjY3FZrMV7tuzZw/169fHw8Oj0msWkTIymaDf48b22g8gO7XEw5rU9eWe/tGA0QgxI0eNEEUqVX4O/F7w/83u90B4G+fWI1LFFEyLiFSxlMxcbpuxlkU7E/FwM/P+rZ25uXtjZ5clIiIubMKECUyfPp1PP/2UnTt3Mn78eDIyMhgzZgwAo0ePZuLEiYXHjx8/nlOnTvHQQw+xZ88e5s6dy3/+8x/uu+8+Z70FEbmQVldCaCvISYV108972L0DookI9uZYajZvL95bhQWKuKBVb8Op/Wp4KC5LwbSISBU6lprFiGmr2XAoGX8vNz6/sxvDLir5Y9IiIiJVZeTIkbzxxhs899xzdOjQgS1btjBv3rzChohxcXEcO3as8PiIiAjmz5/P+vXrufjii3nwwQd56KGHePJJ/VItUm2ZzdD3MWN79VTIzSjxMC93Cy9ceREAM5YfIPZ4elVVKOJaUuJg2ZvG9tBXwUtLXInrMdlrQEeDtLQ0AgMDSU1N1Vp0IlJjxR5PZ/SMdRxNzSY8wJNP7+xGq3r6mSYirk33ea5L117ECaz5MLWrMUNz6KvQ6/7zHjr20/Us2nmcXtF1mTW2u5acE3G02bfArl+hSR+441etLS21Smnv8zRjWkSkCmw8lMwN01ZzNDWbqFBfvhvfS6G0iIiIiFQtixv0mWBsr3ob8rLPe+jzV16Ep5uZVftO8uvWY+c9TkTKYe9CI5Q2WYzmpAqlxUUpmBYRqWQxOxO55aM1pGTm0SEiiG/v6UWjOj7OLktEREREXNHFIyEwAk4nwubPz3tYRLAP9w1sBsArc//itBohijhGfg78/oSx3WO8Gh6KS1MwLSJSib7eEM/dn28kO8/GgJahfDmuO8G+Hs4uS0RERERclZsH9H7I2F45BfJzz3vo3f2iaFLXh8S0HKYs2lNFBYrUcoUND+tB/387uxoRp1IwLSJSCex2O+8tieWJb7ditdm5rlNDpo/ugo+Hm7NLExERERFX1/E28AuH1HjYOue8h3m5W3jhKqMR4syVB9mdoEaIIhWSfKio4eEwNTwUUTAtIuJgNpudF3/5i9fm7QbgX/2jeHNEe9wt+pErIiIiItWAuxf0etDYXjHZaIp4HgNbhjG0TThWm53nftqO3W6voiJFaqH5T0F+FkT2hbbXO7saEadTSiIi4kA5+VYemrOFT1YdBOCZy1sz8bLW6mIuIiIiItVLlzHgHWwsKbDj+3889Lkr2+DlbmbtgVP8/OfRKipQpJbZs8BoeGh2U8NDkQIKpkVEHOR0Tj53fbKBX/48ipvZxFsjOzC2b5SzyxIREREROZeHL/S8z9he9gbYbOc9tFEdHx4Y1ByAV+buJC07ryoqFKk98rKLGh52vwfCWju3HpFqQsG0iIgDnEjPYdSHq1kRm4SPh4WZd3Tlmo4NnV2WiIiIiMj5dRsHnoGQtBt2/fKPh47t25SmIb6cSM/hrYV7q6hAkVpi1TuQfAD868OAJ51djUi1oWBaRKSCDp3M4IZpq9h+JI1gXw++GteDfi1CnV2WiIiIiMg/8wqE7v8ytpe9Dv+wfrSnW1EjxE9XH2TnsbSqqFCk5ks+BMvfMLaHvgKe/s6tR6QaUTAtIlIB24+kcv37qzh0MpNGdbz59p6etI8IcnZZIiIiIiKl02M8ePhBwjbYu+AfD+3fIpTL2tZTI0SRspg3EfKz1fBQpAQKpkVEymlVbBKjPlxD0ulcWtcP4PvxvYgK9XN2WSIiIiIipecTDF3vMraXvvaPs6YBnr2iDd7uFtYfTOb7TUeqoECRGmzPAtg9t6Dh4RtqeCjyNwqmRUTK4detR7n943WczsmnR1Qwc/7Vg7AAL2eXJSIiIiJSdj3vBzcvOLIBDiz9x0MbBHnz4GCjEeKk33eSmqVGiCIlOrvhYY/xENbKufWIVEMKpkVEyuiTlQd44KvN5FntDG9Xj0/v7EaAl7uzyxIRERERKR+/MOh8h7G97I0LHn5Xn6ZEhfqSdDqX/y3cU7m1idRUq94uanjY/9/OrkakWipXMD116lQiIyPx8vKie/furFu37rzH5uXl8dJLLxEdHY2Xlxft27dn3rx55S5YRMRZ7HY7r8/fxQu//IXdDqN7NuGdmzrh6WZxdmkiIiIiIhXT60Ewu8PB5XBo9T8e6uFm5uWr2wLw2eqD7DiaWhUVitQcyQdh+ZvG9rBX1fBQ5DzKHEzPmTOHCRMm8Pzzz7Np0ybat2/PsGHDOH78eInHP/PMM3zwwQe88847/PXXX9xzzz1ce+21bN68ucLFi4hUlXyrjX9/t5Wpf+wD4NFLWvDiVRdhMWuNMBERERGpBQIbQsdbjO3lF5413btZCFdcXB+bHZ79cTs2mxohihQ60/CwaT+46DpnVyNSbZU5mJ48eTLjxo1jzJgxtGnThmnTpuHj48PMmTNLPP7zzz/nqaeeYvjw4URFRTF+/HiGDx/Om2++WeHiRUSqQlaulXu+2MjXGw5jNsH/XdeOBwY3x6TGFSIiIiJSm/R+GEwWiF0ERzZd8PBnLm+Dj4eFTXEpfLvpcOXXJ1IT7JkPu38zGh5e9roaHor8gzIF07m5uWzcuJEhQ4YUDWA2M2TIEFavLvmjPjk5OXh5FW8I5u3tzYoVK8pRrohI1UrJzOXWGWtZtPM4nm5mpt3amVHdGju7LBERERERxwtuChffaGyXYq3peoFePDzEaIT4f7/vIjVTjRDFxRVreHivGh6KXECZgumkpCSsVivh4eHF9oeHh5OQkFDiOcOGDWPy5Mns3bsXm83GwoUL+f777zl27Nh5XycnJ4e0tLRiDxGRqnY0JYsR01az8VAyAV5ufDG2O0MvqufsskREREREKk+fCYAJds+FhO0XPHxM76Y0D/PjVEYubyzYXfn1iVRnK6cY60v7N4D+Tzi7GpFqr1zND8tiypQpNG/enFatWuHh4cH999/PmDFjMJvP/9KTJk0iMDCw8BEREVHZZYqIFLM3MZ3r31/F3uOnqRfgxTf39KJrZLCzyxIRERERqVyhLeCia4zt5RdegtPdYualgkaIX6w9xLbDaoQoLurUAVgx2dhWw0ORUilTMB0SEoLFYiExMbHY/sTEROrVK3kWYWhoKD/++CMZGRkcOnSIXbt24efnR1RU1HlfZ+LEiaSmphY+4uPjy1KmiEiFbDx0ihumreZYajbRob58d28vWtbTTYWIiIiIuIi+jxlfd/wASXsveHjP6Lpc3aEBdjs8+5MaIYqLKtbw8FpnVyNSI5QpmPbw8KBz587ExMQU7rPZbMTExNCzZ89/PNfLy4uGDRuSn5/Pd999x9VXX33eYz09PQkICCj2EBGpCrsT0rnlo7WkZuXRsXEQ397Ti4ZB3s4uS0RERESk6tRrCy2HA3ZYPrlUpzw1vDV+nm5siU/h6w2aXCYuZvc82PO70fBw+BtqeChSSmVeymPChAlMnz6dTz/9lJ07dzJ+/HgyMjIYM2YMAKNHj2bixImFx69du5bvv/+e/fv3s3z5ci699FJsNhtPPKG1dkSk+vnvvF1k59noGVWXWWO7U8fXw9kliYiIiIhUvX4Fs6a3zjHWzL2A8ICiRoj/nbeL5IzcSixOpBrJyypqeNjzPght6dx6RGqQMgfTI0eO5I033uC5556jQ4cObNmyhXnz5hU2RIyLiyvW2DA7O5tnnnmGNm3acO2119KwYUNWrFhBUFCQw96EiIgjrDtwisW7jmMxm/jPde3w8XBzdkkiIiIiIs7RsDNEDwa7FVa8VapTbu8VSctwf5Iz83hdjRDFVaycAimHjIaH/TQJU6QsTHa7vdov/pSWlkZgYCCpqala1kNEKoXdbmfEtNVsOJTMTd0aM+m6ds4uSUTEJeg+z3Xp2ovUAIdWw8eXgsUDHtwCgQ0veMq6A6e48YPVmEzw4729aR8RVOllijjNqQMwtTtYc2DEJ1pbWqRAae/zyjxjWkSkNvpj93E2HErG083MQ4ObO7scERERERHna9ITmvQBay6seqdUp3RrGsx1HRsWNkK0qhGi1GbznjRC6ab9oc01zq5GpMZRMC0iLs9ms/PaPOOjhnf0jqReoJeTKxIRERERqSbOrDW98RM4fbxUpzw5vBX+nm5sPZzK7PVxlVebiDPt/h32zAOzuxoeipSTgmkRcXk//3mUXQnp+Hu5Mb5/tLPLERERERGpPqIGQMMukJ8Fq6eW6pQwfy8mDG0BwGvzdnNKjRCltsnLgt//bWz3vA9CWzi3HpEaSsG0iLi03Hwbby40Zkvf0z+aIB8PJ1ckIiIiIlKNmEzQ73Fje/1HkHmqVKfd1qMJresHkJqVx2vzdlVigSJOsOIto+FhQMOi/3+ISJkpmBYRlzZ7fRzxp7II9fdkTO9IZ5cjIiIiIlL9tBgG4e0g9zSsnVaqU9wsZl6++iIAZq+PZ1NccmVWKFJ1Tu2HFf8ztoe9Cp5+zq1HpAZTMC0iLisjJ5+3Y2IBeHBwc3w83JxckYiIiIhINWQyFa01vXYaZKeW6rQukcHc0LkRAM/+qEaIUkv8XtDwMGqAGh6KVJCCaRFxWR+vPEDS6RwaB/swskuEs8sREREREam+Wl8FIS2NUHr9R6U+7cnLWuHv5caOo2l8ufZQJRYoUgV2/w575xsNDy97XQ0PRSpIwbSIuKTkjFw+WLofgEeHtsDDTT8ORURERETOy2yGvo8a26unQm5GqU4L8fPk8WEtAXh9/m6STudUVoUilSsvC35/wtjudb8aHoo4gJIYEXFJ05buIz0nn1b1/Lny4gbOLkdEREREpPprez3UaQqZJ2HjJ6U+7ZbuTbioQQBp2fn83+9qhCg11Ir/QUocBDRSw0MRB1EwLSIu51hqFp+sOgjAvy9thdmsj1+JiIiIiFyQxQ36TjC2V74NedmlO81s4uVr2gLw7cbDbDh4qrIqFKkcp/bDireM7WGvgoevU8sRqS0UTIuIy3k7Zi85+Ta6RQYzoGWos8sREREREak5Lh5lzBg9nQBbvij1aZ0a1yns6/LsTzvIt9oqq0IRx7Lb4fd/FzQ8HAhtrnZ2RSK1hoJpEXEp+06c5usNhwF44tKWmNSsQkRERESk9Nw8oM/DxvaKt8CaV+pTn7i0JYHe7uw8lsYXa9QIUWqI3b/D3gVGw8Phango4kgKpkXEpUxesAerzc6Q1mF0iQx2djkiIiIiIjVPx1vBLxxS42HrnFKfVtfPkycuNRohvrlgDyfS1QhRqrncTGO2NECvByCkuXPrEallFEyLiMvYdjiVuduOYTLBYwWdwUVEREREpIzcvY2QDmD5m2CzlvrUUV0bc3GjQNJz8pn0285KKlDEQVb8D1LPNDx8zNnViNQ6CqZFxGW8Nt/oAH5Nh4a0qhfg5GpERERERGqwzmPAO9hoCrfjh1KfZjGbePnqtphM8P3mI6zdf7ISixSpgJP7YOVbxval/1HDQ5FKoGBaRFzCqtgklu9Nwt1i4pEhLZxdjoiIiIhIzebpBz3vNbaXvQG20jczbB8RxKiujQF47qcd5KkRolQ3hQ0PcyF6ELS+ytkVidRKCqZFpNaz2+38d/5uAG7u1pjGdX2cXJGIiIiISC3Q7W7wDIQTO2HXr2U69YlhLanj487uxHQ+W61GiFLN7P4NYhcaDQ8vU8NDkcqiYFpEar35OxL5Mz4FHw8L9w9SswoREREREYfwCoTudxvby143ZpmWUh1fD/59aSsA/rdwD4lp2ZVRoUjZ5WbC708a270fhJBmzq1HpBZTMC0itVq+1cYbC4zZ0nf1aUqov6eTKxIRERERqUW6jwd3X0jYCnsXlunUG7tE0D4iiNM5+fxHjRClulgxuajhYd9HnV2NSK2mYFpEarXvNx8h9vhpgnzcGdcvytnliIiIiIjULr51oeudxvay18o0a9psNvFKQSPEn7YcZfU+NUIUJzu5D1ZOMbYvnaSGhyKVTMG0iNRa2XlW3lq4B4D7BjQjwMvdyRWJiIiIiNRCPR8ANy84vB4OLCvTqe0aBXJL9zONELerEaI4T7GGh4Oh9ZXOrkik1lMwLSK11qy1cRxNzaZegBe39Wzi7HJERERERGon/3DodLuxvez1Mp/++NBWBPt6sPf4aT5ZedCxtYmU1q65RsNDiwcMV8NDkaqgYFpEaqX07Dym/hELwMNDmuPlbnFyRSIiIiIitVjvB8HsDgeXQ9yaMp0a6OPOk5cZjRDfWrSHhFQ1QpQqlpsJ8woaHvZ6EOpGO7ceERehYFpEaqWPlh/gVEYuUSG+3NC5kbPLERERERGp3QIbQYebje1lb5T59Bs6NaJT4yAycq28MvcvBxcncgHL34TUeAiMUMNDkSqkYFpEap2k0zl8tHw/AI8Na4mbRT/qREREREQqXZ9HwGQxlkM4urlMp5rNJl6+pi1mE/y69RgrY5MqqUiRvzm5D1a9bWxfOgk8fJxbj4gLUVojIrXO1D9iyci10q5hIJe1refsckREREREXENwU2g3wtgux6zpixoEMrpnJGA0QszNVyNEqWR2O/z2uNHwsNkQaHWFsysScSkKpkWkVok/lcmsNXEA/PvSVpjUsEJEREREpOr0nQCYYNevkFj2JTkeuaQFIX4e7DuRwYwVBxxfn8jZdv0K+2KMhoeXvaaGhyJVTMG0iNQqby3aS67VRu9mdenTPMTZ5YiIiIiIuJbQltDmamN7edlnTQd6uzPxstYAvB2zl6MpWY6sTqRIbgbMm2hsq+GhiFMomBaRWmNPYjo/bD4MwOPDWjm5GhERkZpl6tSpREZG4uXlRffu3Vm3bt15j/3kk08wmUzFHl5eXlVYrYhUa/0eM75u/x6S9pb59Os6NaRrZB2y8tQIUSqRGh6KOJ2CaRGpNd6YvxubHS69qB4dIoKcXY6IiEiNMWfOHCZMmMDzzz/Ppk2baN++PcOGDeP48ePnPScgIIBjx44VPg4dOlSFFYtItVavHbS4DLDDiv+V+XSTycRLV7fFYjbx27YElu054fgaxbUlxcKqd4ztS/9PDQ9FnETBtIjUCpviklnwVyJmEzw2rIWzyxEREalRJk+ezLhx4xgzZgxt2rRh2rRp+Pj4MHPmzPOeYzKZqFevXuEjPDy8CisWkWrvzKzpP2dDctn/cNW6fgC3FzRCfOHnHeTkWx1YnLg0ux1+P9Pw8BJodbmzKxJxWQqmRaTGs9vt/Pf3XQDc0LkRzcL8nVyRiIhIzZGbm8vGjRsZMmRI4T6z2cyQIUNYvXr1ec87ffo0TZo0ISIigquvvpodO3b84+vk5OSQlpZW7CEitVijLhA1EOxWWPlWuYZ4+JLmhPp7sj8pg4+WqxGiOMjOX2Df4oKGh/9Vw0MRJ1IwLSI13rK9Saw9cAoPNzMPDangbGmbDbZ8CbvmQs5pxxQoNcep/bBhprEeYlaKs6sREakSSUlJWK3Wc2Y8h4eHk5CQUOI5LVu2ZObMmfz000988cUX2Gw2evXqxeHDh8/7OpMmTSIwMLDwERER4dD3ISLVUL/Hja+bv4C0o2U+PcDLnaeHG40Q31m8l8PJmY6sTlzR2Q0Pez+khociTubm7AJERCrCZrPz2jxjtvToHk1oGORdsQHXf2R8rAvA4glN+0KLS6H5UKjTpILVSrVjzYP4tbBnHuyZD0l7ip4zWaBJL2gxzPjfQN1mmk0hIlKgZ8+e9OzZs/D7Xr160bp1az744ANefvnlEs+ZOHEiEyZMKPw+LS1N4bRIbRfZGxr3grhVxnq+l04q8xBXd2jAV+viWHvgFC//+hcf3NalEgoVl7H8TUg7DIGNoc+ECx8vIpVKwbSI1Ghztx1jx9E0/DzduHdgs4oNlpUMSwpuln1CIDMJYhcZD4CwNkUhZaOuYLZU7PXEOTJPGdd0zzzja3Zq0XNmN4joARknIGk3HFxuPBY8A8FRxrVvMcz4BcvNw3nvQUTEgUJCQrBYLCQmJhbbn5iYSL169Uo1hru7Ox07diQ2Nva8x3h6euLp6VmhWkWkBur/OHx+LWz42AgC/ULLdLrJZOLla9py2ZTlzN+RyB+7jzOwZVglFSu1WlIsrHzb2L5MDQ9FqgMF0yJSY+VZbUxeaMxwvbtfFMG+FQwKl70BWacgtDXcswJO7SuaSRu3Bo7/ZTxW/A+86xizqFsMg+jB4B1U8TcklcNuhxO7iq5l/Fqw24qe96lrXMvmQyF6UNG1PLUf9iwwzju4wvh+zXvGw8Mfmg0ygupml5T5FywRkerEw8ODzp07ExMTwzXXXAOAzWYjJiaG+++/v1RjWK1Wtm3bxvDhwyuxUhGpkaIGQsPOcGQjrJkKQ14o8xAtwv25s3ck05cf4IWfd9Dz4bp4uWuSiJTBmYaHtjzjvr+l/nslUh0omBaRGuubDYc5kJRBXV8P7uzTtGKDndoP6z40toe+AhY3CG1pPHo/ZMyy3bfYCCn3LjRmV2+dYzy05EP1k5cNh1YYQfSeeZASV/z58LZF16th55JnvwdHQY97jEdOOuxfArvnwd75xozqv34yHpiM5j5nxgtvq+svIjXOhAkTuP322+nSpQvdunXjrbfeIiMjgzFjxgAwevRoGjZsyKRJxieLXnrpJXr06EGzZs1ISUnh9ddf59ChQ4wdO9aZb0NEqiOTyVhr+qtRsG469HoQfILLPMxDQ1rw859HOXQykw+X7efBwc0roViptXb+XNDw0FMND0WqEQXTIlIjZeVamRJjzJa+f1Az/Dwr+ONs0QtgzTVmPzcfcu7zPsHQ7gbjYc2Hw+uKZuCe2KUlH6qD9ATYu8C4Jvv+gLyMoucsnhDV37gmzYdBUBnXNPX0h9ZXGg+bDY5uLrj+8yBhKxxebzwWvwIBDYtC6qb9wL2C656LiFSBkSNHcuLECZ577jkSEhLo0KED8+bNK2yIGBcXh9lc1Dc9OTmZcePGkZCQQJ06dejcuTOrVq2iTZs2znoLIlKdtbgUwttB4jZjMsiAJ8s8hJ+nG09f3oYHv9rM1D9iubZjQyKCtRSD/IPMU5C4HRJ3FC3h0fsh4/c1EakWTHa73e7sIi4kLS2NwMBAUlNTCQgIcHY5IlINTFu6j//7fRcNg7xZ/Fh/PN0q8FG+Q6vh40vBZIZ7VkJ4GX+pPnWgIBAtWPLBmlv0nJZ8qDw2GyT8WTQr+ujm4s/71y8eEHv4Vk4daUcLaphvzKrOzyp6zs27eCAe2LByahCpwXSf57p07UVczI4f4Js7wCsQHt4OXmX//73dbueWj9ayat9JhrQO56Pb1QhRMBqaJ+01AugzQXTiDkg/Wvy4oMZw71qtLS1SBUp7n6dgWkRqnNTMPPq+tpi07HzeHNGe6zs3Kv9gNht8NBiOboLOd8CVUypW3JklH/bMM9Ynzjh+1pNa8qHCcjOK//ueTij+fMPORbPV611c9f++eVnGHyf2zDOW/Ug7XPz5eu0K6rsUGnSCs2Yfirgq3ee5Ll17ERdjs8J7PSBpDwx+HvpOKNcwscfTufSt5eTb7My4vQuDW4c7uFCp1k4fLwqfEwq+nthlrB1dkjqRxu9d4RcZv+8FNKjKakVcloJpEam1Xpu3i/eW7KNFuB+/P9QPi7kC4ePWb+D7seDhBw9uBj8Hdvi22eDY5qIZvcf+LP68lnwoneRDRTPSDywHa07Rcx5+ED2waEa6fzX6xcRuN26Uzyz5cng9cNZ/cn1DixpoRg0s16whkdpA93muS9dexAX9ORt++JfRfPrhbeX+RNuk33fywdL9RAR7s/CR/mqEWBvlZUPS7qLZz2fC6IwTJR/v4W+Ez/UKQujwthDW2liST0SqnIJpEamVjqdl0+/1P8jOszF9dBcuaVOBIDIvC97pYsxqHfwc9H3UcYWWJO1o8TWQteRDyWxWI8Q9E+ge/6v480FNoOVlxr9Vk97g5umcOssqI8lonLlnntF4JSet6DmzO0T2LprtrXXvxIXoPs916dqLuCBrPrzTCVIOwbBJ0PPecg2TkZPPkMlLOZaazUODm/PIJS0cXKhUGbvd+D3p78twJO0Bu7WEE0xGs/kz4XP4RcYjqLE+jSpSjSiYFpFa6dkft/P5mkN0ahzEd+N7YarIzceyN2DxyxAYAfevr9oZyxda8iG8HbR0oSUfslJgX4wRRO9dAFnJRc+ZzNC4Z9Hs8pAWNf+m05oHcauN97v7dzi1r/jzIS2K3m9ED7CoV7HUXrrPc1269iIuauMn8MtDRj+QB7eAu1e5hvlt2zHunbUJDzczCx/pR5O6ldRPRBwnNxNO7Cy+DEfidshOKfl4ryBjKbwz4XP4RRDaWmtEi9QACqZFpNY5dDKDwW8uJd9mZ/bdPegRVbf8g6UnGrM1ck/DdR/BxSMcV2hZ2e3GrOAzM4Tj11FsyQefkKIlH6IH1Y4lH+x2OBlb9J4PrSo+I8IrCJpfYgSz0YPAJ9hppVaJpDP/FvOMwNqWX/ScVyA0G1KwXMmQ2v9vIS5H93muS9dexEXl58DbHSHtCFw+GbreVa5h7HY7o2euY/neJAa2DGXmHV0rNmlFHMduh5S4s2ZAF3w9uY9iv+ecYbIYEzMKA+i2xpIc/vVr/oQUERelYFpEap2HZm/mpy1H6d8ilE/v7FaxwX55yJit0bAz3LWoes1IzkiC2EVGSBkbc+6SD016FS35UDfaeXWWVX4uxK0qWnP71P7iz4e2Kpol3Kib684SzkoxlvrYu8B4ZJ4ses5khojuRf9Ooa10sy41nu7zXJeuvYgLW/sB/P4EBDaGBzeBxb1cw+w7cZpL31pGntXOh7d1ZuhF9RxcqFxQTjok/lV8GY7EHZCbXvLxPiEF60C3LVqKI7RlzVmeT0RKRcG0iNQqO46mcvnbKwD49YE+tG0YWP7BEnfAtD5gt8Gd86FxDwdVWQnOXvJhzzxjlvHZ6jY3QsqWlxmBZTlv6ivN6RMQW7Cucuzi4jeoZndo2tcIWJsPheCmzquzurJZ4cjGopnliduLPx/UuOiPFJF9dUMvNZLu81yXrr2IC8vLgrcuhozjcPV70PGWcg91pjF6wyBvFk3oj7eHGiFWCpsVkg8WD6ATthnrhZfE7G5MoqjXtvhMaEc2mxeRakvBtIjUKmM+Xscfu09wZfsGvHNTx4oN9vm1xozUNlfDjZ85psCqkhQLewtC6kOrzl3yIXpwQdB7iXOWfLDbjZvVM0Hq4Q0U+7iebxi0GGrUGDVAXbLLKiWu4I8U8+HAMrDmFD3n7gvRAwsaaA4Ff80YkppB93muS9dexMWtfBsWPgvB0Ua/F3P5AuXM3HwumbyMIylZPDCoGY8ObengQl1QVvJZs6ALgujjOyEvs+Tj/RsUD5/DL4KQ5tVv0oyIVBkF0yJSa6zdf5KRH67BzWxi0YT+RIZUoLHJ3kUw63qweMB9ayE4ynGFVrXsVCNgP9Mw8O9LPjTqVrTkQ1jrylvyIS/LCEnPhNFpR4o/X7990aze+h2r17IpNVluBuxfWvCHivmQfqz48w06Fv2712uvf3eptnSf57p07UVcXM5peKutEYJePwPa3VDuoeZtT+CeLzbiYTEz/5F+NK3I7wuuxJpvfCLz78tw/L0x+xluXsbvFWcH0GEXgW8Fev+ISK2kYFpEagW73c4N01az8VAyt3RvzKvXtiv/YNZ8mNYbTuyCXg/A0FccV6izXWjJh8DGBUt+XApN+pS7+3mh1CNFgej+pZCfVfScm3fxmbsBDSr2WnJhdjskbC1a8uXIxuLP+9UrPlPdQ7+sSfWh+zzXpWsvIix9Hf54BcLawD0ry/2HdLvdzh0fr2fpnhP0axHKp2PUCPEcmaeM+8XCAHo7HN9V/BN4ZwtsXDQL+sya0MFR5Z7ZLiKuRcG0iNQKMTsTuevTDXi5m1n6+EDCAyoQqK6fAXMngHcwPLgZvIMcVme1kxJfPDj++5IPUQMKZlMPK92SDzYbHN1UEHzPM9aTO1tAIyP0bnEpRPYBd2+Hvh0po/REYxb9nnmw7w/Iyyh6zuJZtLZ3i2HGOtUiTqT7PNelay8iZKXAW+2MZt8jZ0HrK8o91IGkDIb9bxm5VhvTbu3EpW3rO67Omsxuh3UfwvynwZZ37vPuvhDepmgGdHhbY1Z0bf5dSUQqnYJpEanxrDY7w6csZ3diOvf0j+bJy1qVf7DsNHi7I2QmwWWvQ/e7HVdodZebUXypjb8v+VC/w1lLbXQomqmSnQb7/yha0zgz6ayTTBBx9lIhbSpvqRCpmPwcOLiiaDb13xvUhLUpuo6NumoWjFQ53ee5Ll17EQEg5mVY/oax/NvdSyt0T/nmgt28sziWBoFeLHq0Pz4ebg4stAbKz4G5j8Lmz43vg5pA/YvPCqEvgqBILfkmIg6nYFpEarwfNh/mkTl/EuDlxvInBhHoU4HmGYtegBX/g7rN4d7VrtuI44JLPoRDsyHGOtEHVxafVeEZAM0Kmis2GwK+IVVbu1Sc3Q4ndhf9kSJ+DdhtRc97BxuNM0Na6A8Nrq7XQ2Cpml/mdZ/nunTtRQSAjJPGWtN5mXDLt8a9SDll5Vq55H9LOZycxb0Donni0gpMbKnp0hNhzq1weJ3Rf2bIi8ZyhrrHE5EqoGBaRGq03Hwbg95cwuHkLJ64tCX3DmhW/sGSD8G7XY3lLG6aYyw5IYb0RIhdWLTkQ+7p4s/XbVY0m7pxT9cN9GurzFMQG2Nc/9iFRkNNEYCnEyu+Fn0p6T7Pdenai0ih+U/D6neN5t13LahQeLrwr0TGfbYBd4uJeQ/3IzrUz4GF1hBHNsHsWyD9KHgGwg0zofkQZ1clIi6ktPd5Lv65FhGprr5aF8fh5CzC/D0Z06tpxQaLedEIpZv2MwJWKeIfDh1vNR75OXBopbEmtW+oEUiHVOAPAlL9+QTDxSOMhzUf4tcaAXVG0oXPldpNS7qIiEhV6vUArJtuzO49uNy4by+nIa3DGNQqjMW7jvP8Tzv4/K5urtUI8c858MuDkJ9tfApu1Fe6pxeRakvBtIhUOxk5+byzeC8ADw5ujrdHBQKS+PWw/TvABENf1UfX/ombJ0QPMh7ieixuENnbeIiIiIhUJf960Gk0rJ8Oy16vUDBtMpl4/so2rIhNYkVsEr9tS+Dyi12gEaLNCoueh1XvGN83HwbXTwevQOfWJSLyD7TCvYhUOzNXHCDpdC5N6vowsmtE+Qey22H+U8Z2x1uMRh8iIiIiIlL99H4IzG5G0+64tRUaqkldX+4dEA3Ay7/+RUZOviMqrL6yUuDLG4tC6T4T4KavFEqLSLWnYFpEqpXkjFw+XLYfgEeHtsTdUoEfUzt+MD4O6O4LA59xUIUiIiIiIuJwQRHQ/iZje/kbFR7unv7RNA72ISEtm7cLPo1ZK53YA9MHQewicPM21pMe8ryW5RKRGkHBtIhUK+8v3Ud6Tj5t6gdwRbsKfOQuL9v4KBsYsy8CXODjeyIiIiIiNVmfR8Bkhr0L4OiWCg3l5W7hhavaADBj+QH2JqY7oMBqZs98+GgwnNoHAY3gznnQ9npnVyUiUmoKpkWk2jiWmsUnqw4C8PilLTGbK7Ae9LoPICUO/BtAr/sdU6CIiIiIiFSeutHQboSx7YBZ04NahTOkdTj5NjvP/bQDu91e4TGrBbsdlk+GL0dCTho07gl3L4EGHZxdmYhImSiYFpFqY8qiveTm2+jWNJgBLULLP1BGEiwruJEd/Bx4+DqmQBERERERqVx9JgAm2PkLHN9Z4eGev7INnm5mVu8/yS9bj1W8PmfLzYTv7oKYFwE7dB4Do38Gvwr8/iQi4iQKpkWkWog9fpqvN8QD8O9LW2IyVWC29JJJxsyB+u3h4pEOqlBERERERCpdWCtoc5WxvfzNCg8XEezDfQObATB1cWzNnjWdEg8zh8H274xGkZdPhivfAjcPZ1cmIlIuCqZFpFqYvHA3NjsMaR1O5ybB5R/oxG7Y8LGxPfRVMOvHnIiIiIhIjdL3MePr9u/g5L4KD3d7r0i83M3sTkxnc3xKhcdzikOrYfpASNgKPnVh9E/Q9S5nVyUiUiFKbETE6bYeTuG3bQmYTPD4sJYVG2zBs2C3QqsroGlfxxQoIiIiIiJVp/7F0OJSsNuMtZQrKNDbneEFjdW/WhtX4fGq3IaP4dMrIeMEhLcz1pOO7OPsqkREKkzBtIg43evzdwNwbceGtKznX/6B9i2GvfONj7Vd8pKDqhMRERERkSp3Ztb01tmQfKjCw93UrTEAv249Rnp2XoXHqxLWPJj7KPz6MNjyoM01cNd8CGrs7MpERBxCwbSIONXK2CSW703C3WLikSEtyj+QzQrznzG2u44zOnqLiIiIiEjNFNEVogaALR9WTqnwcF2a1KFZmB9ZeVZ+2nK04vVVtowk+OwaWP8RYIJBz8KIT9TYXURqFQXTIuI0drud1+btAuCW7k2ICPYp/2BbZsHxHeAVBP2fcEyBIiIiIiLiPP0eN75u/hzSjlVoKJPJxKiuEQDMXl/Nl/NI2AYfDoRDK8DDH276Cvo9BhVpEC8iUg0pmBYRp5m/I4E/D6fi42Ep7JRdLjnpsPgVY7v/v8GnAs0TRURERESkemjSGxr3BGsurHqnwsNd16kRHhYz24+ksf1IqgMKrAQ7foAZQyE1Duo0hbGLoOVlzq5KRKRSKJgWEafIt9oK15Ye26cpof6e5R9s5RQ4nQjBUdB1rIMqFBERERERpzKZjJnCABtmGstbVECwrwdDLwoH4Kt11WzWtM1mTLb55g7Iy4SogTBuMYS1cnZlIiKVRsG0iDjF95uOsO9EBnV83BnbL6r8A6UeLpo9cclL4ObhmAJFRERERMT5ogdDg46QnwWrp1Z4uDNNEH/acpTM3PwKj+cQ2Wkw5xZY9rrxfc/74ZZv9UlQEan1FEyLSJXLzrPyv0V7ALhvYDMCvNzLP1jMy5CfbXzMr9UVDqpQRERERESqBZOpaK3pddMhK7lCw/WMqkvjYB9O5+Tz69aKrVvtECf3wYxLYPdvYPGEa6bBsFfB4ubsykREKp2CaRGpcl+sOcSx1GzqB3pxa48m5R/oyCbYOtvYHvaqmoGIiIiIiNRGLS6D8LaQmw5rP6zQUGaziZFnmiA6ezmPfYth+iA4sQv86sGY36HDTc6tSUSkCimYFpEqlZ6dx9Q/YgF4ZEgLvNwt5RvIbof5TxvbF48yPt4nIiIiIiK1j9kMfR81tte8ZzQ/r4ARnRthMZvYFJfCnsSKjVUudruxLMkX10N2CjTsAncvgUadq74WEREnUjAtIlVq+vIDJGfmER3qy3WdGpZ/oJ2/QNwqcPOGwc86rkAREREREal+2lwNdZsbQe76GRUaKizAi8GtwgAnNEHMy4Yf74X5T4HdBh1ugTvmQkD9qq1DRKQaUDAtIlUm6XQOHy3fD8BjQ1viZinnj6D8XFj4nLHd6wEIbOSgCkVEREREpFoyW4pmTa9+F3IzKzTcmSaIP2w+QnaetaLVlU7aMfjkcvjzSzCZYdgkuHoquHtVzeuLiFQzCqZFpMq8uziWzFwrFzcK5NK29co/0PrpkHwA/MKh90OOK1BERERERKqvdjdAUBPIOAGbPq3QUP1ahNIg0IuUzDzm70hwUIH/4PAG+HAAHNkAXkFw6/fQ8171yRERl6ZgWkSqRPypTGatPQTAvy9tham8N2CZp2Dpf43tQc+Ap5+DKhQRERERkWrN4g59HjG2V06B/JzyD2U2MaKL0QSx0pfz2PIlfHwZnE6A0NZw9x8QPbByX1NEpAZQMC0iVeJ/i/aQZ7XTp1kIvZuFlH+gpa9BdqrRlbvDLY4rUEREREREqr8ON4N/A0g/BltmVWioG7tGYDLBmv2nOJCU4aACz2LNh3lPwY/jwZoLLS+HsQshOMrxryUiUgMpmBaRSrc7IZ0fNh8B4PFhLcs/UFKssYwHwNBXjHXmRERERETEdbh5Fi3nt+J/YM0r91ANg7zp3yIUgNnrHTxrOvMUzLoB1kw1vu//bxj5BXj6O/Z1RERqMAXTIlLp3liwG7sdhrerR/uIoPIPtPA5sOVD82H66JuIiIiIiKvqNBp8QyElDrZ9U6GhRnU1miB+t/Ewufk2R1QHx3fC9EGw/w9w94ERn8LAp8CsCEZE5Gz6qSgilWrjoWQW/pWI2QQTLqnAbOkDy2H3XDBZYOjLjitQRERERERqFg8f6Hm/sb38TbBZyz3U4NZhhPh5knQ6l5idiRWvbddv8NEQo1l7UGO4awFcdE3FxxURqYUUTItIpbHb7fx33i4ARnSOoFlYORsV2mww/ylju8udEFqBgFtERERERGq+rneBVxCcjIW/fiz3MO4WMyO6NALgq/Xx5a/Hboelr8PsmyD3NET2hXFLoF678o8pIlLLKZgWkUqzdM8J1h04hYebmYeGNC//QFtnQ8JW8AyAAU86rkAREREREamZPP2hx73G9rI3jMks5TSqawQAy/eeIP5UZtkHyM2Ab+6AP14xvu92N9z2A/jWLXdNIiKuoFzB9NSpU4mMjMTLy4vu3buzbt26fzz+rbfeomXLlnh7exMREcEjjzxCdnZ2uQoWkZrBZrPz2rzdANzeswkNgrzLN1BuBsS8ZGz3ewx8QxxUoYiIiIiI1Gjd/2VMXjn+F+z5vdzDNKnrS6/outjt8M2GMs6aTj4EM4Yas7bN7nDlFBj+Oljcy12PiIirKHMwPWfOHCZMmMDzzz/Ppk2baN++PcOGDeP48eMlHv/ll1/y5JNP8vzzz7Nz505mzJjBnDlzeOqppypcvIhUX79uO8Zfx9Lw93Tj3gHNyj/Qqnch/RgENYFu/3JcgSIiIiIiUrN5B0G3ccb2steN5TTKaVQ3owni1xsOk28t5ezrgytg+kBI3G40Y7z9F+h8R7lrEBFxNWUOpidPnsy4ceMYM2YMbdq0Ydq0afj4+DBz5swSj1+1ahW9e/fm5ptvJjIykqFDh3LTTTddcJa1iNRceVYbkxcYs6Xv7hdFHV+P8g2UdgxWvmVsX/IiuHs5pkAREREREakdetwL7j5wdDPsiyn3MMMuCqeOjzsJadks3XPinw+222H9R/DZ1ZB5Euq3h7uXQJOe5X59ERFXVKZgOjc3l40bNzJkyJCiAcxmhgwZwurVq0s8p1evXmzcuLEwiN6/fz+//fYbw4cPr0DZIlKdfb0hnoMnMwnx8+DOPk3LP9DiVyAvEyK6Q5trHFafiIiIiIjUEr4hRoN0MJoPlnPWtKebhes6FTRBXPcPy3nk58KvD8PcR8GWD21vgDHzILBRuV5XRMSVlSmYTkpKwmq1Eh4eXmx/eHg4CQkJJZ5z880389JLL9GnTx/c3d2Jjo5mwIAB/7iUR05ODmlpacUeIlIzZOVambJoLwD3D2yGr6db+QY6thW2zDK2h74KJpODKhQRERERkVql5/1g8YT4NcbyGuV0UzejCeIfu4+TmFZCX6zTx+Gzq2DjJ4AJhrwI138EHj7lfk0REVdWruaHZbFkyRL+85//8N5777Fp0ya+//575s6dy8svv3zecyZNmkRgYGDhIyIiorLLFBEH+WTVQY6n59Cojjc3dW9cvkHsdljwNGA3ZiBEdHVojSIiIiIiUosE1IdOtxnby14v9zDNwvzp0qQOVpv93CaIR7fAhwMhbrXRcPHmr6HPw5pAIyJSAWUKpkNCQrBYLCQmJhbbn5iYSL169Uo859lnn+W2225j7NixtGvXjmuvvZb//Oc/TJo0CZut5IYCEydOJDU1tfARH1/Grrgi4hSpmXm8vyQWgAmXtMDTzVK+gfbMgwPLjFkPQ553YIUiIiIiIlIr9X4IzG5wYCnEl7+n1U0FTRDnbIjHZitYFmTbtzDzUkg7DHWbwdgYaDHUEVWLiLi0MgXTHh4edO7cmZiYooYCNpuNmJgYevYseZH/zMxMzObiL2OxGGGV/TxrP3l6ehIQEFDsISLV37Rl+0jLzqdluD9Xd2hYvkGsebDgGWO7570QVM5Z1yIiIiIi4jqCGkP7Ucb2sjfKPczwdvXx93Ij/lQWK/cmwqIX4Lu7ID8Lml1ihNKhLRxTs4iIiyvzUh4TJkxg+vTpfPrpp+zcuZPx48eTkZHBmDFjABg9ejQTJ04sPP7KK6/k/fffZ/bs2Rw4cICFCxfy7LPPcuWVVxYG1CJS8x1Py+bjlQcAeHxYSyzmcn6kbcNMOBkLPiHQZ4IDKxQRERERkVqtzwQwmWHvfDj2Z7mG8PawcG3HhviTSdBPt8OK/xlP9H4Ibp4D3kGOq1dExMWVOZgeOXIkb7zxBs899xwdOnRgy5YtzJs3r7AhYlxcHMeOHSs8/plnnuHRRx/lmWeeoU2bNtx1110MGzaMDz74wHHvQkSc7u3Fe8nOs9G5SR0Gtw4r3yBZybDk/4ztQU+Dlz4tISIiUlWmTp1KZGQkXl5edO/enXXrSvdR+NmzZ2Mymbjmmmsqt0ARkQupGw1trze2KzBrenTzfH70eJZ2mWuwu3nBdR/BJS+BWZPrREQcyWQ/33oa1UhaWhqBgYGkpqZqWQ+RauhgUgZDJi8l32bn63/1pFvT4PINNP9pWP0uhLaCe1aCxc2xhYqISLWj+7zqYc6cOYwePZpp06bRvXt33nrrLb755ht2795NWNj5/+B88OBB+vTpQ1RUFMHBwfz444+lfk1dexGpFMd3wns9jO1710BY67Kdv3cRfHsn5KRy1B7Mmq5vc90VVzq+ThGRWqy093llnjEtIvJ3kxfuId9mZ2DL0PKH0qf2w9qCT1IMfVWhtIiISBWaPHky48aNY8yYMbRp04Zp06bh4+PDzJkzz3uO1Wrllltu4cUXXyQqKqoKqxUR+QdhraF1QZC8fHLpz7PbYeUU+HIE5KRyok4Hrs55hXd3+5+3P5aIiFSMgmkRqZAdR1P5+c+jADw2rGX5B1r0AtjyIHowNB/imOJERETkgnJzc9m4cSNDhhT999dsNjNkyBBWr1593vNeeuklwsLCuOuuu0r1Ojk5OaSlpRV7iIhUin6PG1+3fwsn9134+Lws+P5uWPgc2G3Q8Ta8x/5Ghkdd9p/IYN2BU5Vbr4iIi1IwLSIV8vr83QBc1b4BFzUILN8gh1bDXz8ZjUqGvuLA6kRERORCkpKSsFqthT1jzggPDychIaHEc1asWMGMGTOYPn16qV9n0qRJBAYGFj4iIiIqVLeIyHnVbw/Nhxkh85nmheeTegQ+vgy2fQ0mCwx/A656Bz9fX65q3wCA2evjq6BoERHXo2BaRMptzf6TLNl9AjeziQmXtCjfIDYbzH/K2O40GsLbOK5AERERcbj09HRuu+02pk+fTkhISKnPmzhxIqmpqYWP+HgFPSJSifo9Znz98ytIiSv5mLi18OEAOLoZvINh9I/QbRyYTACM6tYYgN+2HSM1M6/yaxYRcTFaxFVEysVut/PavF0AjOoWQWSIb/kG2v4tHN0EHn4w8GkHVigiIiKlERISgsViITExsdj+xMRE6tWrd87x+/bt4+DBg1x5ZVEzMJvNBoCbmxu7d+8mOjr6nPM8PT3x9PR0cPUiIucR0Q2a9ocDS421oy9/s/jzmz6DXycYywmGXQQ3fQl1Iosd0r5RIK3q+bMrIZ0fNh/mjt5Nq65+EREXoBnTIlIuMTuPsykuBS93Mw8Oal6+QfKyYNGLxnbfCeAX5rgCRUREpFQ8PDzo3LkzMTExhftsNhsxMTH07NnznONbtWrFtm3b2LJlS+HjqquuYuDAgWzZskVLdIhI9XFmrelNn0PaMWPbmge/PQE/P2CE0q2vhLsWnBNKA5hMJm4qmDU9e328miCKiDiYgmkRKTOrzV64tvSdvZsSFuBVvoFWT4W0wxAYAT3udWCFIiIiUhYTJkxg+vTpfPrpp+zcuZPx48eTkZHBmDFjABg9ejQTJ04EwMvLi7Zt2xZ7BAUF4e/vT9u2bfHw8HDmWxERKRLZByJ6gDUHVr8Lmafgi+tg3QfG8wOfhhGfgaffeYe4pkNDPN3M7EpIZ0t8StXULSLiIrSUh4iU2U9bjrA7MZ1Ab3f+1f/cj+qWSnpiUSOSwc+Du7fjChQREZEyGTlyJCdOnOC5554jISGBDh06MG/evMKGiHFxcZjNmtMiIjWMyWTMmp51PWyYCTt/gZRDxjKC134Ara+44BCBPu5c3q4+328+wux18XRsXKcKChcRcQ0mew34LEpaWhqBgYGkpqYSEBDg7HJEXFpOvpXBby7lcHIW/760FeMHlDOY/uUh2PgJNOwMdy0C/bIrIuKSdJ/nunTtRaRK2O1Gg8NjW4zv60TCqK/K1HR93YFT3PjBanw8LKx7egh+nprjJyLyT0p7n6ckSETK5Ku1cRxOziLM35M7ekWWb5DEHUazEYBh/1EoLSIiIiIilcNkgiEvgMUTogfBuD/KFEoDdI2sQ3SoL5m5Vn7ecrRy6hQRcUFKg0Sk1E7n5PPO4lgAHhrSHG8PS9kHsdth/tNgt0Gbq6FxDwdXKSIiIiIicpbogfDvg3DbD+ATXObTTSYTo7qeaYIY5+DiRERcl4JpESm1mSsOcDIjl8i6PtzYJaJ8g8Qugv1/gMXDmLkgIiIiIiJS2Tx8KnT6dZ0a4m4xsfVwKjuOpjqoKBER16ZgWkRK5VRGLtOX7Qfg0aEtcbeU48eHNR8WPGNsd/8XBEc5sEIREREREZHKUdfPk6EX1QNg9rp4J1cjIlI7KJgWkVJ5f0ks6Tn5XNQggMvb1S/fIJs+hRO7wDsY+j7m2AJFREREREQq0U0Fy3n8uOUIWblWJ1cjIlLzKZgWkQs6mpLFp6sPAfDEpa0wm01lHyQ7Ff74j7E9YCJ4BzmuQBERERERkUrWK7ouEcHepGfnM3fbMWeXIyJS4ymYFpELmrJoL7n5Nro3DaZf85DyDbJ8MmQmQd3m0GWMYwsUERERERGpZGbzWU0Q16kJoohIRSmYFpF/FHv8NN9sNNZQe+LSVphM5ZgtnXwI1rxvbA99GSzuDqxQRERERESkaozo3AiL2cSGQ8nsTUx3djkiIjWagmkR+UdvLtiNzQ6XtAmnc5M65Rsk5kWw5kDTftDiUscWKCIiIiIiUkXCArwY1CoMgNnr1QRRRKQiFEyLyHn9GZ/C79sTMJng8WEtyzdI/HrY/h1ggqGvQnlmXIuIiIiIiFQTN3WLAOD7TYfJyVcTRBGR8lIwLSLn9fr83QBc17ERLcL9yz6A3Q7znzK2O94C9S92YHUiIiIiIiJVr3+LMOoHepGcmcf8HYnOLkdEpMZSMC0iJVqxN4kVsUl4WMw8PKR5+QbZ8QMcXgfuPjDwGccWKCIiIiIi4gQWs4kRXYxZ02qCKCJSfgqmReQcdrud1+bvAuCWHo2JCPYp+yB52bDoeWO798MQUN9xBYqIiIiIiDjRjV0aYTLBqn0nOZiU4exyRERqJAXTInKOedsT2Ho4FR8PC/cNbFa+QdZOg5Q48G8Ave53bIEiIiIiIiJO1KiOD/2ahwJqgigiUl4KpkWkmHyrjdcXGGtLj+0bRYifZ9kHyUiC5W8a24OfBQ9fB1YoIiIiIiLifGeaIH678TB5VpuTqxERqXkUTItIMT9sPsL+ExnU8XFnXN+m5RtkySTISYP67eHiUY4tUEREREREpBoY3DqcED9Pkk7nELNTTRBFRMpKwbSIFMq32pj6RywA9/SPxt/LveyDHN8FGz42toe+Cmb9mBERERERkdrH3WLmhs6NAPhqnZbzEBEpKyVGIlLol61HOXgykzo+7tzao0n5Bln4LNit0OoKaNrXsQWKiIiIiIhUI6O6Gst5LNt7gsPJmU6uRkSkZlEwLSIAWG123l1szJYe2zcKX0+3sg+ybzHsXQBmNxjyooMrFBERERERqV4iQ3zpGVUXux2+3nDY2eWIiNQoCqZFBIDfth1j34kMAr3dGd2zHLOlbVaY/4yx3XUchDRzbIEiIiIiIiLV0KiCJojfbIjHarM7uRoRkZpDwbSIYLPZeWfxXgDu6tO0fGtLb/4Cju8AryDo/4RjCxQREREREammhl1UjyAfd46lZrN0z3FnlyMiUmMomBYR5u9IYE/iafy93Li9V2TZB8hJhz9eNbb7PwE+wQ6tT0REREREpLrycrdwXUc1QRQRKSsF0yIuzm6383bB2tJjekUS6F2O2dIrp8DpRAiOMpbxEBERERERcSE3FSznsXjXcY6nZTu5GhGRmkHBtIiLW7TzODuPpeHrYeHOPk3LPkDqYVj1jrF9yUvg5uHYAkVERERERKq55uH+dG5SB6vNzjcb1QRRRKQ0FEyLuDC73c7bMcba0rf3iiTIpxyhcsxLkJ8NTXpDqyscXKGIiIiIiEjNMKqrMWt69vo4bGqCKCJyQQqmRVzYkt0n2HYkFR8PC2P7RpV9gCObYOscY3voK2AyObZAERERERGRGuLyi+vj7+lG/KksVu076exyRESqPQXTIi7KbrczpWC29G09mhDsW8bZ0nY7zH/a2L54FDTs5OAKRUREREREag4fDzeu7tgAgK/Wxzm5GhGR6k/BtIiLWr43iS3xKXi5m8s3W3rnLxC3Cty8YfCzji9QRERERESkhhnVtTEAC3YkcPJ0jpOrERGp3hRMi7igs9eWvrlbE0L9Pcs2QH4uLHzO2O51PwQ2cnCFIiIiIiIiNU/bhoG0axhIntXO95uOOLscEZFqTcG0iAtavf8kGw4l4+Fm5l/9yzFbev10SD4AfuHQ+2GH1yciIiIiIlJTjepmNEH8an0cdruaIIqInI+CaREXdGa29E1dIwgP8CrbyZmnYOl/je1Bz4Cnn4OrExERERERqbmuat8AHw8L+09ksP5gsrPLERGpthRMi7iYtftPsmb/KTwsZu4ZEF32AZb+F7JTIbwtdLjF8QWKiIiIiIjUYP5e7lx5sdEEcfY6NUEUETkfBdMiLuadxbEA3NClEfUDvct2clIsrP/I2B76CpgtDq5ORERERESk5juznMfcbcdIzcxzcjUiItWTgmkRF7LxUDIrYpNwM5sY378cs6UXPge2fGg+DKIHOr5AERERERGRWqBDRBCt6vmTk2/jxy1qgigiUhIF0yIu5J3FxtrS13dqRESwT9lOPrAMds8FkwWGvlwJ1YmIiIiIiNQOJpOJUV0LmiCuUxNEEZGSKJgWcRF/xqewZPcJLGYT9w4s42xpmw3mP21sdxkDoS0dX6CIiIiIiEgtcm3HRni6mdmVkM6fh1OdXY6ISLWjYFrERZyZLX1Nh4Y0qetbtpO3zoaEreAZAAMmVkJ1IiIiIiIitUugjzvD29UH1ARRRKQkCqZFXMD2I6ks2nkcswnuK+ts6dwMiHnJ2O73GPiGOL5AERERERGRWujMch4//3mU0zn5Tq5GRKR6UTAt4gLeXRwLwJXtGxAV6le2k1e9A+nHIKgJdPtXJVQnIiIiIiJSO3VrGkxUqC+ZuVZ++fOos8sREalWFEyL1HK7EtKYtyMBkwnuH9isbCenHYOVU4ztIS+Au5fD6xMREREREamtzm6CqOU8RESKUzAtUsu9UzBbeni7+jQP9y/byYtfgbxMaNQNLrq2EqoTERERERGp3a7v1Ah3i4k/D6fy19E0Z5cjIlJtKJgWqcX2Jqbz27ZjADwwqIyzpY/9CVtmGdvD/gMmk4OrExERERERqf3q+nkytE09AGav16xpEZEzFEyL1GLv/hGL3Q7DLgqnVb2A0p9ot8P8pwE7tL0eIrpWWo0iIiIiIiK13ahuxnIeP2w+Qlau1cnViIhUDwqmRWqp/SdOFzbXeGBQ87KdvGceHFwOFk8Y/HwlVCciIiIiIuI6ekeHEBHsTXp2fuGnWkVEXJ2CaZFaauof+7DZYUjrMNo2DCz9idY8WPCMsd3zXqjTpHIKFBERERERcRFms4mRXQqaIGo5DxERQMG0SK106GQGP245ApRjtvTC5+FkLPiEQJ8JlVCdiIiIiIiI6xnRJQKL2cT6g8nEHk93djkiIk6nYFqkFnrvj31YbXb6twilfURQ6U/c8DGsmWpsX/4meJVhXWoRERERERE5r/AALwa2DANg9rp4J1cjIuJ8CqZFapn4U5l8t+kwAA8OLsNs6f1L4bfHjO2BT8NF1zi+OBERERERERd2U0ETxO82HSYnX00QRcS1KZgWqWWmLd1Hvs1On2YhdG5Sp3QnJcXC17eBLR/ajYB+j1dukSIiIiIiIi6of4tQ6gV4kZyZx4Idic4uR0TEqRRMi9QiR1Oy+HqD8ZGwUs+WzjwFX94I2anQqBtc9S6YTJVYpYiIiIiIiGtys5i5sUsjQE0QRUQUTIvUIh8s3Uee1U6PqGC6NQ2+8An5ufD1aDi1DwIbw6hZ4O5V+YWKiIiIiIi4qBu7RmAywcrYkxw6meHsckREnEbBtEgtcTwtm6/WF8yWHlSK2dJ2O/z2KBxcDh7+cPMc8Aur5CpFRERERERcW6M6PvRtHgrAnPVqgigirkvBtEgt8cGy/eTm2+jSpA49o+te+ITVU2HTZ2Ayww0zIbxN5RcpIiIi1dbUqVOJjIzEy8uL7t27s27duvMe+/3339OlSxeCgoLw9fWlQ4cOfP7551VYrYhIzXZTV6MJ4jcbD5NntTm5GhER51AwLVILnEjPYdbaQ4CxtrTpQmtE7/4dFjxjbA/7D7QYWskVioiISHU2Z84cJkyYwPPPP8+mTZto3749w4YN4/jx4yUeHxwczNNPP83q1avZunUrY8aMYcyYMcyfP7+KKxcRqZkGtw4nxM+DE+k5LN5V8s9aEZHaTsG0SC3w0fL9ZOfZ6BARRN/mIf98cMI2+PYuwA6dx0D3e6qkRhEREam+Jk+ezLhx4xgzZgxt2rRh2rRp+Pj4MHPmzBKPHzBgANdeey2tW7cmOjqahx56iIsvvpgVK1ZUceUiIjWTh5uZ6zsXNEFcpyaIIuKaFEyL1HAnT+fw2WpjtvRDF5otnZ4IX46CvAxo2h+Gvw4Xml0tIiIitVpubi4bN25kyJAhhfvMZjNDhgxh9erVFzzfbrcTExPD7t276dev33mPy8nJIS0trdhDRMSVjeraGICle05wNCXLydWIiFQ9BdMiNdyMFQfIyrPSrmEgA1qGnv/AvCyYfROkHYa6zeDGT8HiXnWFioiISLWUlJSE1WolPDy82P7w8HASEhLOe15qaip+fn54eHhw+eWX884773DJJZec9/hJkyYRGBhY+IiIiHDYexARqYmahvjSIyoYmx2+3qAmiCLiehRMi9RgKZm5hbOlHxjU7Pyzpe12+PFeOLIRvOvAzV8bX0VERETKyd/fny1btrB+/XpeffVVJkyYwJIlS857/MSJE0lNTS18xMcrhBERuambMWv66/XxWG12J1cjIlK13JxdgIiU38yVBzmdk0/r+gFc0ib8/Acu+T/Y8T2Y3WHkF1A3uuqKFBERkWotJCQEi8VCYmJisf2JiYnUq1fvvOeZzWaaNWsGQIcOHdi5cyeTJk1iwIABJR7v6emJp6enw+oWEakNhl1UjyAfd46mZrNs7wkGtgxzdkkiIlVGM6ZFaqjUrDw+XnkAgAf/abb0tm9h6f8Z21f8DyL7VFGFIiIiUhN4eHjQuXNnYmJiCvfZbDZiYmLo2bNnqcex2Wzk5ORURokiIrWWl7uFazs2BNQEUURcj2ZMi9RQn646SHp2Pi3C/Rh20XlmM8WvN5bwAOj1IHS6reoKFBERkRpjwoQJ3H777XTp0oVu3brx1ltvkZGRwZgxYwAYPXo0DRs2ZNKkSYCxXnSXLl2Ijo4mJyeH3377jc8//5z333/fmW9DRKRGuqlbYz5eeZCYncc5np5NmL+Xs0sSEakSCqZFaqD07DxmrDBmS98/qDlmcwmzpVPijGaH1hxoORyGvFC1RYqIiEiNMXLkSE6cOMFzzz1HQkICHTp0YN68eYUNEePi4jCbiz5smZGRwb333svhw4fx9vamVatWfPHFF4wcOdJZb0FEpMZqEe5Pp8ZBbIpL4duNh7l3QDNnlyQiUiVMdru92q+un5aWRmBgIKmpqQQEBDi7HBGne29JLK/N201UqC8LH+mP5e/BdE46zBgGx3dAeDu4cx54+jmnWBERkX+g+zzXpWsvIlLk6w3xPPHtVprU9eGPRweUPPlIRKSGKO19ntaYFqlhMnLy+Wi5MVv6gUHNzg2lbVb49i4jlPYLh5tnK5QWERERERGpxq64uD7+nm4cOpnJmv0nnV2OiEiVUDAtUsPMWnuIUxm5RNb14cqLG5x7wMLnYO98cPOCUV9BYKOqL1JERERERERKzcfDjas6GL/ffbU+3snViIhUDQXTIjVIVq6VD5ftB+C+gc1ws/zt/8IbP4HV7/5/e3ceV2Wdv3/8OufAYQcXNtkCNTV3BSG10sqyPadyrTRbp9QWZvqms+j0mylrbMopLMtssVyzmmxfKC0LRTFzxwUVUNlcAEG2c87vD5Sy1EDh3BzO6/l4nMec7nMfuJgbZj5dvPnctc+HvSRFxTs3IAAAAADgrIxOjJEkfb4pT4fKqgxOAwBNj2IacCEL0rNVdLRKUa19NKxP5MkvZq2QPv5T7fNL/yp1v8n5AQEAAAAAZ6V7ZJC6RwaqymbXe+tyjY4DAE2OYhpwERXVNr28Ypek2mlpz19OSxftlJbcLtlrpB7DpUseNSglAAAAAOBsjepXOzW9MD1bDofD4DQA0LQopgEXsWRtjgpKKxUR5K2b+/5i3+jyQ9KCEVJFsRSVKN2QIpm4gzMAAAAAuJobe0fIx9OiXYVlWrv3sNFxAKBJUUwDLqCyxqaXltdOS99/aUdZPY7/6NZUSUvGSod2SUEx0qj5kqe3gUkBAAAAAGcrwNtT1/VsJ6l2ahoAWjKKacAFLM3I1YHiCoUHemtEwvFpaYdD+uRP0p7vJKu/NGaR5B9qbFAAAAAAwDkZdfwmiJ9sPKDiY9UGpwGApkMxDTRz1Ta7Xvymdlr6vkHt5eVhqX0hbZa0bp5kMku3vCaFdTMwJQAAAACgMfSNaaVOYf6qqLbrg/X7jI4DAE2GYhpo5t5ft0/7jhxTsL+XRh//zbkyP5W++Fvt86FPSp2GGhcQAAAAANBoTCbTL26CmMNNEAG0WBTTQDNWY7Mr5ZudkqQ/Dmovb0+LlLdRWnqXJIcUP15K+qOxIQEAAAAAjeqmvpGyepi19UCJNuQWGx0HAJrEWRXTs2bNUmxsrLy9vZWUlKT09PTTnjt48GCZTKbfPK699tqzDg24iw/W71f2oXK19bNqTFKMVJovLRglVZdJcYOka2ZIJpPRMQEAAAAAjaiVr1XXdA+XJC1aw00QAbRMDS6mFy9erOTkZE2bNk3r1q1Tr169NHToUBUUFJzy/Pfee08HDhyoe2zatEkWi0XDhw8/5/BAS2azO+qmpe+5pL18TdXSotFSSa7UtqM04k3J4mlwSgAAAABAUzhxE8Rl6/errLLG4DQA0PgaXEw/++yzuueeezR+/Hh17dpVs2fPlq+vr1577bVTnt+mTRuFh4fXPb788kv5+vpSTAO/46MN+7W7qEytfD11W1KM9L8HpH0Zkk9racyS2v8EAAAAALRISXFt1D7YT2VVNn34036j4wBAo2tQMV1VVaWMjAwNGTLk5w9gNmvIkCFKS0ur18eYO3euRo0aJT8/v9OeU1lZqZKSkpMegDux2x164evaaem7L4qTf9oz0ub3JLOHNOItqW0HgxMCAAAAAJqSyWTSyH7RkqSFa3IMTgMAja9BxXRRUZFsNpvCwsJOOh4WFqa8vLzffX96ero2bdqku++++4znTZ8+XUFBQXWP6OjohsQEXN6nm/K0s+CoAr09dGerDGnFU7UvXPecFHexseEAAAAAAE5xc3yUPC0m/ZRzRFsPMLQHoGU5q5sfnq25c+eqR48eSkxMPON5U6ZMUXFxcd0jJ4ffDMJ91E5L75Ak/aXnUfl+/GDtCwMelPqONTAZAAAAAMCZgv29dEXX2uHARencBBFAy9KgYjo4OFgWi0X5+fknHc/Pz1d4ePgZ31tWVqZFixbprrvu+t3P4+XlpcDAwJMegLv4Yku+tuWV6nyvwxqx8zHJVil1vkYa8g+jowEAAAAAnGxUv9qbIL7/4z5VVNsMTgMAjadBxbTValV8fLxSU1PrjtntdqWmpqp///5nfO8777yjyspK3XbbbWeXFHADDkfttLSfjmmB33MylxdKYT2km+ZIZovR8QAAAAAATnZRx2BFtfZRSUWNPtl4wOg4ANBoGryVR3JysubMmaM333xTW7du1f3336+ysjKNHz9ekjR27FhNmTLlN++bO3euhg0bprZt2557aqCF+npbgbbuP6JZXrMUUr5T8g+TxiySvPyNjgYAAAAAMIDZbNLIhNp7by1KZ6tTAC2HR0PfMHLkSBUWFmrq1KnKy8tT79699dlnn9XdEDE7O1tm88l9d2ZmplauXKkvvviicVIDLZDD4dDzqTs0xWOBBpvWSR7e0qiFUlCU0dEAAAAAAAYanhCt577arvQ9h7Sz4Kg6hjK8BMD1NbiYlqSJEydq4sSJp3xt+fLlvznWuXNnORyOs/lUgNtYsb1QFxx4X/d4flJ7YNhLUlS8saEAAAAAAIYLD/LWZV1C9dXWAi1ek62/XtvV6EgAcM4avJUHgMbncDj09afv6J8er9ceuPSvUvebjA0FAAAAAGg2TtwE8d11+1RZw00QAbg+immgGchYt1Z/OvwveZpsquhyk3TJo0ZHAgAAAAA0I4M7hygs0EuHyqr05ZZ8o+MAwDmjmAaMVn5IEZ+MU5CpXDl+3eV980uSyWR0KgAAAABAM+JhMWsEN0EE0IJQTANGqqlS8bwxirDtU64jWF63LpQ8vY1OBQAAAABohkYkRMtkklbuLFL2wXKj4wDAOaGYBozicEif/ElBeWk66vDW/7o8q9CIGKNTAQAAAACaqeg2vrqoY7AkafHabIPTAMC5oZgGjJI2S1o3TzaHSY/YJukPV19pdCIAAAAAQDM3OrF2oOmdtbmqsdkNTgMAZ49iGjBC5qfSF3+TJD1Rc5uC+96gyFY+BocCAAAAADR3Qy4IU1s/qwpKK/X1tgKj4wDAWaOYBpwtb6O09C5JDs2vuVxvOq7WA4M7Gp0KAAAAAOACrB5m3RIfJUlatIabIAJwXRTTgDOV5ksLRknVZdri3UfTasbppj5Rim7ja3QyAAAAAICLGNkvWpK0PLNAB4qPGZwGAM4OxTTgLNXHpEVjpJJcVQa116gj98tu8tCES5mWBgAAAADUX/sQfyXFtZHdIS1Zk2t0HAA4KxTTgDM4HNIHE6R9ayWf1vp/gdNUIn/d2DtSscF+RqcDAAAAALiYEzdBXLI2Rza7w+A0ANBwFNOAM6x4Wtr0rmT20J7LX9b8HZ4ymcS0NAAAAADgrFzVPVxBPp7ad+SYvttRaHQcAGgwimmgqW1cKi2fXvv8uuf078zg2qc9I9Qx1N/AYAAAAAAAV+XtadEf+kRKkhalcxNEAK6HYhpoSjlrpP89UPt8wCRlRvxBn2zMkyRNuoxpaQAAAADA2TuxncdXW/NVWFppcBoAaBiKaaCpHMmWFo2WbJVS52ukIY8r5ZudkqRreoSrU1iAwQEBAAAAAK6sc3iA+sS0Uo3doaUZ3AQRgGuhmAaaQmWptGCUVFYohfWQbpqjnUXH9NGG/ZKkiZeeb3BAAAAAAEBLMLpf7dT04jXZcji4CSIA10ExDTQ2u016926pYLPkHyaNWSR5+WvWNzvlcEhXdA1T14hAo1MCAAAAAFqA63q1k7+Xh/YcLFda1kGj4wBAvVFMA43ty6nS9s8kD29p1EIpKEp7isr0wfp9kqQHL2NaGgAAAADQOHytHrqhd4QkboIIwLVQTAONKeMNKS2l9vmwl6SoeEnSrG92yu6QLusSqh5RQcblAwAAAAC0OCe28/hsU54Ol1UZnAYA6odiGmgsWSukj/9U+/zSv0rdb5Ik5Rwq13s/1k5LT7qso1HpAAAAAAAtVI+oIHWLCFSVzV73758A0NxRTAONoWintOR2yV4j9RguXfJo3UsvLt8pm92hi88PVp+Y1gaGBAAAAAC0VKMSa6emF6VzE0QAroFiGjhX5YekBSOkimIpKlG6IUUymSRJ+44c09KMXEnSQ5eztzQAAAAAoGnc2DtCPp4W7Sg4qnXZh42OAwC/i2IaOBe2amnJWOnQLikoRho1X/L0rnt59vJdqrY5NKBDWyXEtjEwKAAAAACgJQv09tS1PdtJkhZyE0QALoBiGjhbDof0cbK05zvJ6i+NWST5h9a9nFdcocVrahcDDzItDQAAAABoYqMToyVJH23Yr5KKaoPTAMCZUUwDZyttlrRunmQyS7e8JoV1O+nl2St2qcpmV2JcG13Yvq1BIQEAAAAA7qJvTGudH+qvimq7Pli/3+g4AHBGFNPA2cj8VPrib7XPr3xC6jT0pJcLSiq0MD1bkvTgZUxLAwAAAACanslkOukmiADQnFFMAw2Vt1FaepckhxQ/Xrrw/t+c8sq3WaqssatvTCsN7Mi0NAAAAADAOW7qEymrxazN+0u0MbfY6DgAcFoU00BDlOZLC0ZJ1WVS3CDpmhmSyXTSKUVHKzV/9fFp6cvPl+lXrwMAAAAA0FRa+1l1VfdwSdLCNUxNA2i+KKaB+qo+Ji0aI5XkSm07SiPelCyevznt1e9261i1Tb2igjSoU4gBQQEAAAAA7mzU8ZsgLlu/X2WVNQanAYBTo5gG6sPhkD6YIO1bK/m0lsYsqf3PXzlUVqV5aXskSZMuY1oaAAAAAOB8/du3VWxbXx2trNHHGw4YHQcAToliGqiPFU9Lm96VzB7SiLekth1OedprK3ervMqmru0CdfkFoU4OCQAAcPZmzZql2NhYeXt7KykpSenp6ac9d86cObr44ovVunVrtW7dWkOGDDnj+QAA5zKZTBrZr/YmiGznAaC5opgGfs/GpdLy6bXPr3tOirv4lKcVl1frjR/2SGJvaQAA4FoWL16s5ORkTZs2TevWrVOvXr00dOhQFRQUnPL85cuXa/To0frmm2+Ulpam6OhoXXnlldq3b5+TkwMATueW+Ch5mE36MfuIMvNKjY4DAL9BMQ2cSc4a6X8P1D4fMEnqO/a0p77+w24draxRl/AAXdk1zEkBAQAAzt2zzz6re+65R+PHj1fXrl01e/Zs+fr66rXXXjvl+fPnz9cDDzyg3r17q0uXLnr11Vdlt9uVmprq5OQAgNMJCfDSFcf/3XRhOlPTAJofimngdI5kS4tGS7ZKqfM10pDHT3tqSUW1Xlu5W1Lt3tJmM9PSAADANVRVVSkjI0NDhgypO2Y2mzVkyBClpaXV62OUl5erurpabdq0Oe05lZWVKikpOekBAGhaoxJrt/N4b12uKqptBqcBgJNRTAOnUlkqLRgllRVKYT2km+ZIZstpT5/3wx6VVNSoY6i/ru4e7sSgAAAA56aoqEg2m01hYSf/xVdYWJjy8vLq9TEee+wxRUREnFRu/9r06dMVFBRU94iOjj6n3ACA33dxx2BFtvJRSUWNPt3ETRABNC8eRgdolvK3SIVbjU4BI61fIBVslvzDpDGLJC//0556tLJGr9ZNS3dkWhoAALiVp556SosWLdLy5cvl7e192vOmTJmi5OTkun8uKSmhnAaAJmY2mzSyX7Se/XK7nvh4q6Ja+6pf7On/ugUAnIli+lS2Lvv5ZndwXx7e0qiFUlDUGU97K22vjpRXq32wn67rGeGkcAAAAI0jODhYFotF+fn5Jx3Pz89XePiZ/xLsmWee0VNPPaWvvvpKPXv2POO5Xl5e8vLyOue8AICGGdc/Vp9uytPWAyUaM2eV/nFDN92adJ7RsQCAYvqUWsVIsRcbnQJG8vCS+k+QouLPeFp5VY1e/S5LkjTh0o6yMC0NAABcjNVqVXx8vFJTUzVs2DBJqruR4cSJE0/7vn//+9964okn9PnnnyshIcFJaQEADRXk66l37++vR5du0McbDuiv72/S5v0l+sf13WT1YIdXAMahmD6V3mNqH8DvWLA6WwfLqhTTxlc39mZaGgAAuKbk5GSNGzdOCQkJSkxM1MyZM1VWVqbx48dLksaOHavIyEhNn177V4VPP/20pk6dqgULFig2NrZuL2p/f3/5+59+CzQAgDF8rR5KGd1H3SICNePzTC1Yna0d+aV68dZ4hQTw1ywAjMGvxoCzVFFt0+wVJ6alO8jDwo8TAABwTSNHjtQzzzyjqVOnqnfv3lq/fr0+++yzuhsiZmdn68CBn2+a9dJLL6mqqkq33HKL2rVrV/d45plnjPoSAAC/w2Qy6YHBHfXauH4K8PbQmj2HdUPKSm3IPWJ0NABuyuRwOBxGh/g9JSUlCgoKUnFxsQIDA42OA0iSXv9+tx7/cIsiW/nomz8P5k+gAAA4C6zz3BfXHgCMs6vwqO6dt1a7Csvk5WHWUzf30B/6nPn+SgBQX/Vd59GkAWehdlp6lyTpgUs7UEoDAAAAAFxGhxB/vT9hoC7vEqrKGrseWfyT/vXRFtXY7EZHA+BGaNOAs/BORq7ySyrVLshbt8TzW2UAAAAAgGsJ9PbUnLEJmnhpR0nSqyt3a/wba3SkvMrgZADcBcU00EBVNXa99M1OSdL9gzvIy8NicCIAAAAAABrObDbpz0M768Vb+8rH06LvdhTphpTvlZlXanQ0AG6AYhpooHfX5Wp/cYVCA7w0IiHa6DgAAAAAAJyTa3q003sPDFB0Gx9lHyrXH178Xp9tOvD7bwSAc0AxDTRAtc2uWcenpe8b1EHenkxLAwAAAABc3wXtArVswkUa0KGtyqts+uPb6/Tsl9tltzuMjgaghaKYBhrg/R/3KffwMQX7WzUmMcboOAAAAAAANJrWflbNuzNRdw6MkyQ9n7pD976VodKKaoOTAWiJKKaBeqqx2fXi8Wnpey9pLx8r09IAAAAAgJbFw2LW1Ou76j/De8nqYdZXW/P1hxd/0O6iMqOjAWhhKKaBevpww37tOViuNn5W3Zp0ntFxAAAAAABoMjfHR2nJff0VHuitnQVHdWPKSi3PLDA6FoAWhGIaqAeb3aEXvq6dlr7rojj5eXkYnAgAAAAAgKbVO7qVlk0aqPjzWqukokZ3vrFGs1fsksPBvtMAzh3FNFAPH288oKzCMgX5eGpsf6alAQAAAADuITTAWwvuSdLoxGjZHdJTn27TQ4vW61iVzehoAFwcxTTwO+x2h1K+3iGpdlo6wNvT4EQAAAAAADiPl4dFT/6hh/45rLs8zCYt+2m/bpn9g3IPlxsdDYALo5gGfsfnm/O0Pf+oArw9NG5ArNFxAAAAAABwOpPJpNsvPE/z705SWz+rNu8v0Q0p32tV1kGjowFwURTTwBnY7Q79N7V2Wnr8wDgF+TAtDQAAAABwX0nt22rZpIvUPTJQh8qqdNurqzUvbQ/7TgNoMIpp4Ay+2pqvbXml8rNadOfAWKPjAAAAAABguMhWPnrnvgG6oVeEauwOTf1gsya/u1GVNew7DaD+KKaB03A4HHr++N7S4wbEqpWv1eBEAAAAAAA0Dz5Wi/47qrf+ck0XmU3S4rU5Gv3KKhWUVBgdDYCLoJgGTmN5ZqE27SuRr9Wiuy9ub3QcAAAAAACaFZPJpHsv6aDXxycq0NtD67KP6PqUlVqfc8ToaABcAMU0cAoOx897S99+4Xlq48e0NAAAAAAApzKoU4iWTbxI54f6K7+kUiNeTtPSjFyjYwFo5iimgVP4bkeR1ucckbenmWlpAAAAAAB+R2ywn96fMFBXdA1TVY1df37nJz3+4WbV2OxGRwPQTFFMA7/yy2npMYnnKSTAy+BEAAAAAAA0f/5eHnr5tng9dPn5kqTXv9+jsa+l61BZlcHJADRHHkYHAIzicDhUWFqprKIyZRWWaXfRUWUVlimrqEy7i8pk9TDrvkFMSwMAAAAAUF9ms0mPXNFJF7QLVPKS9fph10HdkLJSc8Ym6IJ2gUbHA9CMUEyjxSuvqvm5cC4sU1bRUe0+/ry0sua077v7ojiFBXo7MSkAAAAAAC3DVd3DFRc8UPfMW6vsQ+W66cUf9MzwXrq2ZzujowFoJiim0SLY7A7lHi6vm37OKqwtn7MKy5RXUnHa95lNUlRrX8UF+6l9iJ/aB/upfYi/OoT4KzyIUhoAAAAAgLPVOTxAyyYO1KSFP+q7HUWasGCdthzooD9d0Vlms8noeAAMRjENl+FwOHSorKqucK4toY8qq6hM2QfLVXWGGyq08bPWls/BfooL8VP7YH91CPFTTFtfeXlYnPhVAAAAAADgPlr5WvX6Hf309GfbNOe73Zr1zS5tPVCqmaN6K9Db0+h4AAxEMY1mp6Lapj0HT+z7XKZdhUfrnhcfqz7t+6weZsW1rZ18jjs++RwX7KcOIX5q5Wt14lcAAAAAAABO8LCY9ddru6pbRJAee3eDvt5WoGGzvtecsQnqEOJvdDwABqGYhiHsdof2Fx+rK5xPTD5nFZZpf/ExORynf29kK5+fy+dgP8WF+Kt9sJ8iWvnIwp8CAQAAAADQLA3rE6kOIf669621yios07CU7/Xf0b11WZcwo6MBMADFNJpUcXm1dhUdPemmgyfK6Mqa02+9EeDtUbvXc/CJCWh/tQ/xU2xbP/lY2XoDAAAAAABX1CMqSMsmXqQH5mdozZ7DuuvNtfrzlZ31wOAOMpkYNgPcCcU0zllljU05h8q1q/BE6fzz1hsHy6pO+z5Pi0kxbXzV/vjEc/uQn7ffaOtn5f+QAAAAAABogUICvDT/7gv1+IebNX91tmZ8nqkt+0s0Y3hP+VqpqgB3wU876sXhcCi/pPKkLTdOTEDnHCqX/Qxbb4QFeql9sP/xmw4eL6CD/RXV2kceFrPzvggAAAAAANAsWD3MeuIPPdQtIkjTlm3SxxsPaFfhUc0Zm6DoNr5GxwPgBBTT+A2b3aEvt+Rry4GSuv2fdxeVqbzKdtr3+FktddPOJ/Z/7hDir9hgP/l78W0GAAAAAAB+a0xSjM4P89f9b6/TtrxS3ZCyUrNu7asBHYKNjgagidEY4iQ2u0MPL16vD3/a/5vXLGaTolv71G29EXd88rl9iJ9CA7zYegMAAAAAADRYv9g2+nDSQN33VoY25Bbr9rnp+tu1F+iOAbF0DUALRjGNOg6HQ399f6M+/Gm/PC0m/eH43XJPTELHtPGV1YOtNwAAAAAAQONqF+SjJff111/e26j3ftynxz/cos37S/SvYd3l7WkxOh6AJkAxDUm1pfS/Pt6qRWtyZDZJ/x3VR9f0aGd0LAAAAAAA4Ca8PS36z4he6hoRqCc/2aqlGbnaWXBUL98er7BAb6PjAWhkjL9CkvTcVzs0d+VuSdLTN/eklAYAAAAAAE5nMpl098XtNe/OJAX5eGp9zhFd98JKZew9bHQ0AI2MYhp65dtdej51hyTp8Ru6aXhCtMGJAAAAAACAO7vo/GAtmzhQncMCVFhaqdGvrNLiNdlGxwLQiCim3dz81Xv15CfbJEmPDu2scQNijQ0EAAAAAAAg6by2fnrvgQG6qlu4qmx2PfbuRk37YJOqbXajowFoBBTTbuz9H3P1t/9tkiTdP7iDJlza0eBEAAAAAAAAP/Pz8tCLt/ZV8hWdJElvpu3Vba+u1sGjlQYnA3CuKKbd1Oeb8/TndzbI4ZDG9T9P/ze0s9GRAAAAAAAAfsNsNunBy8/XnLEJ8vfy0Ordh3RDyvfavL/Y6GgAzgHFtBv6bkehJi34UTa7Qzf3jdK067vJZDIZHQsAAAAAAOC0rugapv9NGKC4YD/tO3JMN7/0g5b9tN/oWADOEsW0m1mz55DumbdWVTa7ru4erqdv7iGzmVIaAAAAAAA0fx1DA/S/CQM1qFOIKqrtenDhj3rq022y2R1GRwPQQBTTbmRjbrHufH2NKqrtGtQpRP8d1UceFr4FAAAAAACA6wjy8dRrd/TTHwd1kCTNXrFLd725RsXHqg1OBqAhaCXdxPb8Uo19bbVKK2uUGNdGs2+Ll9WDyw8AAAAAAFyPxWzS5Ku76PnRfeTtadbyzEINm/W9dhaUGh0NQD2dVTM5a9YsxcbGytvbW0lJSUpPTz/j+UeOHNGECRPUrl07eXl5qVOnTvrkk0/OKjAabu/BMt326modLq9Wr6ggzR2XIB+rxehYAAAAAAAA5+SGXhFa+scBimzlo91FZRo26wd9uSXf6FgA6qHBxfTixYuVnJysadOmad26derVq5eGDh2qgoKCU55fVVWlK664Qnv27NHSpUuVmZmpOXPmKDIy8pzD4/cdKD6mMXNWq6C0Up3DAvTmnYkK8PY0OhYAAAAAAECj6B4ZpGUTByopro2OVtbonnlr9XzqDtnZdxpo1kwOh6NBP6VJSUnq16+fUlJSJEl2u13R0dGaNGmSJk+e/JvzZ8+erRkzZmjbtm3y9Dy7QrSkpERBQUEqLi5WYGDgWX0Md1R0tFIjXk5TVmGZ4oL9tPi+CxUa4G10LAAAgDqs89wX1x4A0NiqbXb966MtejNtryTpqm7h+s+IXvLz8jA4GeBe6rvOa9DEdFVVlTIyMjRkyJCfP4DZrCFDhigtLe2U71m2bJn69++vCRMmKCwsTN27d9eTTz4pm8122s9TWVmpkpKSkx5omOLyat0+N11ZhWWKbOWjt+9OopQGAAAAAAAtlqfFrMdv7K6nb+4hq8Wszzbn6aYXf1D2wXKjowE4hQb9yqioqEg2m01hYWEnHQ8LC9O2bdtO+Z6srCx9/fXXuvXWW/XJJ59o586deuCBB1RdXa1p06ad8j3Tp0/X448/3pBo+IWjlTW64410bT1QomB/L719d5IiW/kYHQsAAAAAAKDJjewXo46hAfrj2xnKzC/V9SkrNX5grCJb+SiilY/Cg7zVLshbvlYmqQEjNflPoN1uV2hoqF555RVZLBbFx8dr3759mjFjxmmL6SlTpig5Obnun0tKShQdHd3UUVuEimqb7nlzrX7MPqJWvp56++5ExQX7GR0LAAAAAADAaeLPa60PJ16k+97O0E85RzTzqx2/OSfIx1PtjpfU4UE+igjyVniQN+U14CQN+ukKDg6WxWJRfv7JdzfNz89XeHj4Kd/Trl07eXp6ymKx1B274IILlJeXp6qqKlmt1t+8x8vLS15eXg2JBtXupTRh/jqlZR2Un9WiN8cnqks4+/UBAAAAAAD3Ex7krcX3Xqj5q7O1Pa9U+4uPKa+4QgeKK3S0skbFx6pVfKxa2/JKT/sxKK+BptOgnxyr1ar4+HilpqZq2LBhkmonolNTUzVx4sRTvmfgwIFasGCB7Ha7zObaLa23b9+udu3anbKUxtmx2R16ZPF6pW4rkJeHWXPv6Kde0a2MjgUAAAAAAGAYb0+L7roo7jfHSyuqdeB4SX3gyDEdKK5QXnEF5TXgRA3+qUhOTta4ceOUkJCgxMREzZw5U2VlZRo/frwkaezYsYqMjNT06dMlSffff79SUlL00EMPadKkSdqxY4eefPJJPfjgg437lbgxu92hKe9t0EcbDsjTYtLLt8frwvZtjY4FAAAAAADQLAV4eyrA21OdwgJOew7lNdC0GvwdP3LkSBUWFmrq1KnKy8tT79699dlnn9XdEDE7O7tuMlqSoqOj9fnnn+uRRx5Rz549FRkZqYceekiPPfZY430VbszhcOifH2/RkrW5Mpuk50f10eDOoUbHAgAAAAAAcGmU10DTMjkcDofRIX5PSUmJgoKCVFxcrMBA9kz+pf98kakXvt4pSXpmeC/dEh9lcCIAAID6Y53nvrj2AAB3Ud/yuj4or+EK6rvO4zvVhc1esauulP5/N3ajlAYAAMBZmzVrlmbMmKG8vDz16tVLL7zwghITE0957ubNmzV16lRlZGRo7969eu655/Twww87NzAAAC6iOUxe94tro6S4NjKZTE3xJQJnhWLaRb21aq+e+nSbJOn/ruqssf1jjQ0EAAAAl7V48WIlJydr9uzZSkpK0syZMzV06FBlZmYqNPS328SVl5erffv2Gj58uB555BEDEgMA0LI4o7xuH+ynUYnRurlvlNr6ezX1lwT8LrbycEHvrctV8pKfJEkTLu2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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Classification Report:\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.93      0.88      0.90        16\n",
+            "           1       0.80      0.89      0.84         9\n",
+            "\n",
+            "    accuracy                           0.88        25\n",
+            "   macro avg       0.87      0.88      0.87        25\n",
+            "weighted avg       0.89      0.88      0.88        25\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Predict sentiment of new reviews\n",
+        "new_reviews = [\"The food was good!\", \"I'm quite satisfied with the service.\"]\n",
+        "new_sequences = tokenizer.texts_to_sequences(new_reviews)\n",
+        "new_padded = pad_sequences(new_sequences, maxlen=100, padding='post')\n",
+        "predictions = model.predict(new_padded)\n",
+        "\n",
+        "print(\"Sentiment Predictions:\", [\"Positive\" if p > 0.5 else \"Negative\" for p in predictions])"
+      ],
+      "metadata": {
+        "id": "guEceo_O8O39",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a0bf24b0-9aae-4498-a964-37e461de4ea4"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 234ms/step\n",
+            "Sentiment Predictions: ['Positive', 'Positive']\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Online Food Delivery Preferences/Model/README.md b/Online Food Delivery Preferences/Model/README.md
new file mode 100644
index 000000000..1370e1de0
--- /dev/null
+++ b/Online Food Delivery Preferences/Model/README.md	
@@ -0,0 +1,157 @@
+## **Online Food Delivery Preferences**
+
+### 🎯 **Goal**
+
+The project focuses on two primary objectives:
+
+Predict whether customers will place future orders using CNN, RNN, and a hybrid RNN+LSTM model based on demographic data such as age, occupation, monthly income, and family size.
+Perform sentiment analysis on customer reviews to better understand customer experiences, using DNN, LSTM, and GRU models to classify the reviews as positive or negative.
+
+
+### 🧵 **Dataset**
+
+Link : https://www.kaggle.com/datasets/benroshan/online-food-delivery-preferencesbangalore-region/data
+
+The dataset consists of 388 entries with 55 columns, providing detailed insights into customer preferences and experiences in online food delivery. Key variables include:
+
+1. **Demographics**: Age, gender, marital status, occupation, income, and education levels.
+2. **Geographical Data**: Latitude, longitude, pin codes.
+3. **Service Preferences**: Meal and medium preferences (e.g., "Medium (P1)", "Meal (P1)"), ease, convenience, and time-saving benefits.
+4. **Delivery Experience**: Factors like restaurant choices, payment options, food quality, tracking systems, and road conditions.
+5. **Challenges**: Common issues such as late deliveries, hygiene concerns, wrong orders, missing items, and delivery delays.
+6. **User Feedback**: Reviews, Google Maps accuracy, politeness of delivery personnel, and the influence of ratings.
+7. **Outcome**: Whether this consumer will buy again or not (e.g., "Output").
+
+### 🧾 **Description**
+
+This project is divided into two main tasks:
+
+Customer Reordering Behavior Prediction:
+For predicting whether a customer will reorder, CNN, RNN, and RNN+LSTM models were used. These models are trained on the structured dataset containing demographic data and behavioral factors.
+
+Sentiment Analysis of Customer Reviews:
+For text-based sentiment analysis, DNN, LSTM, and GRU models were developed. These models aim to classify customer reviews as positive or negative, helping businesses understand customer satisfaction more effectively.
+
+### 🧮 **What I had done!**
+
+1) Data Preprocessing:
+-Removed missing and irrelevant data (e.g., 'Nil' reviews).
+-Tokenized reviews and converted them into sequences suitable for deep learning models.
+
+2) Exploratory Data Analysis (EDA):
+-Analyzed the distribution of customer demographics such as age, income, family size etc.
+-Created visualizations like bar charts and word clouds for reviews to understand sentiment polarity.
+
+3) Model Implementation for Prediction:
+-Built CNN, RNN, and RNN+LSTM models to predict customer reordering behavior.
+-Experimented with different architectures to capture patterns in structured data.
+
+4) Model Implementation for Sentiment Analysis:
+-Developed DNN, LSTM, and GRU models for customer review analysis.
+-These models were optimized to handle varying text lengths and interpret user sentiment effectively.
+
+5) Evaluation and Comparison:
+-Compared models using accuracy, precision, recall, and F1-score.
+-Identified the most accurate models for each task.
+
+### 🚀 **Models Implemented**
+
+For Customer Reordering Prediction:
+CNN (Convolutional Neural Network): Captures local patterns and relationships in structured data such as demographic information.
+RNN (Recurrent Neural Network): Captures sequential dependencies between behavioral factors, though it can suffer from vanishing gradients.
+RNN + LSTM (Hybrid Model): Handles long-term dependencies more effectively than standard RNNs, providing more accurate predictions for reordering behavior.
+
+For Sentiment Analysis:
+DNN (Deep Neural Network): A feedforward network used for simple yet effective sentiment classification.
+LSTM (Long Short-Term Memory): Captures long-term dependencies in reviews, making it suitable for longer texts.
+GRU (Gated Recurrent Unit): An efficient variant of LSTM, offering similar capabilities with fewer parameters.
+
+### 📚 **Libraries Needed**
+
+1. **os**  
+- Provides functions for interacting with the operating system (e.g., file and directory management).  
+
+2. **pandas**  
+   - Used for data manipulation and analysis, especially with DataFrames.  
+
+3. **numpy**  
+   - Supports numerical operations, including working with arrays, matrices, and mathematical computations.  
+
+4. **scipy**  
+   - Provides statistical functions and tools for scientific computing.  
+
+5. **matplotlib**  
+   - A plotting library used for creating visualizations like graphs and charts.  
+
+6. **seaborn**  
+   - A data visualization library built on Matplotlib, offering advanced statistical plots.  
+
+7. **scikit-learn (sklearn)**  
+   - A machine learning library providing tools for:
+     - Data preprocessing (e.g., LabelEncoder, StandardScaler)  
+     - Model evaluation (e.g., classification_report, accuracy_score)  
+     - Train-test data splitting (e.g., train_test_split)  
+
+8. **tensorflow**  
+   - A deep learning framework used to build and train neural networks.  
+
+9. **tensorflow.keras**  
+   - A high-level neural network API within TensorFlow, offering:
+     - Model creation using Sequential  
+     - Neural network layers (e.g., Dense, LSTM, GRU, Dropout)  
+     - Regularization techniques (e.g., L2 regularizers)  
+     - Text processing tools (e.g., Tokenizer, pad_sequences) .
+
+### 📊 **Exploratory Data Analysis Results**
+
+<h2>Demographic  Details</h2>
+![Age Group]
+(https://github.com/Pratzybha/Images/blob/main/Images/AgeGroup.png)
+
+![Demographic  Details]
+(https://github.com/Pratzybha/Images/blob/main/Images/DemographicDetails.png)
+
+<h2>Correlation Calculation</h2>
+![Correlation Calculation]
+(https://github.com/Pratzybha/Images/blob/main/Images/CorrelationCalculation.png)
+
+<h2>Features That Influence Users To Use Online Food Delivery</h2>
+![Features That Influence Users To Use Online Food Delivery]
+(https://github.com/Pratzybha/Images/blob/main/Images/FeaturesOnlineDelivery%201.png)
+
+<h2>Features That Discourage Users To Use Online Food Delivery</h2>
+![Features That Discourage Users To Use Online Food Delivery]
+(https://github.com/Pratzybha/Images/blob/main/Images/FeaturesOnlineDelivery%202.png)
+
+<h2>Time Factor</h2>
+![Time Factor]
+(https://github.com/Pratzybha/Images/blob/main/Images/FeaturesOnlineDelivery%203.png)
+
+### 📈 **Performance of the Models based on the Accuracy Scores**
+
+| Model      | Accuracy |
+|------------|----------|
+| CNN        | 90%      |
+| RNN        | 92%      |
+| RNN + LSTM | 92.3%    |
+| DNN        | 84%      |
+| LSTM       | 92%      |
+| GRU        | 88%      |
+
+
+### 📢 **Conclusion**
+
+The project successfully demonstrates the potential of deep learning models for both customer behavior prediction and sentiment analysis.
+
+The RNN + LSTM Hybrid model provided the best performance for sentiment analysis, achieving an accuracy of 92.3%.
+The LSTM model effectively predicted reordering behavior with 92% accuracy.
+These insights help food delivery companies better understand customer preferences and enhance their service quality.
+
+
+### ✒️ **Your Signature**
+
+Your Name: [Pratibha Balgi]
+
+GitHub: [https://github.com/Pratzybha]
+
+LinkedIn: [https://www.linkedin.com/in/pratibhabalgi2410/]
diff --git a/Online Food Delivery Preferences/requirements.txt.txt b/Online Food Delivery Preferences/requirements.txt.txt
new file mode 100644
index 000000000..0b090dbda
--- /dev/null
+++ b/Online Food Delivery Preferences/requirements.txt.txt	
@@ -0,0 +1,10 @@
+Libraries used -
+
+matplotlib
+numpy
+os
+pandas
+scipy
+seaborn
+sklearn
+tensorflow ​
\ No newline at end of file

From b52d071617ae9a96719ce937547cee1036b0f8c7 Mon Sep 17 00:00:00 2001
From: Pratzybha <84196704+Pratzybha@users.noreply.github.com>
Date: Sun, 27 Oct 2024 09:06:28 +0530
Subject: [PATCH 2/4] Update and rename requirements.txt.txt to
 requirements.txt

---
 .../{requirements.txt.txt => requirements.txt}                  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)
 rename Online Food Delivery Preferences/{requirements.txt.txt => requirements.txt} (72%)

diff --git a/Online Food Delivery Preferences/requirements.txt.txt b/Online Food Delivery Preferences/requirements.txt
similarity index 72%
rename from Online Food Delivery Preferences/requirements.txt.txt
rename to Online Food Delivery Preferences/requirements.txt
index 0b090dbda..e2a5a987e 100644
--- a/Online Food Delivery Preferences/requirements.txt.txt	
+++ b/Online Food Delivery Preferences/requirements.txt	
@@ -7,4 +7,4 @@ pandas
 scipy
 seaborn
 sklearn
-tensorflow ​
\ No newline at end of file
+tensorflow ​

From df76048bce8d6c3b089f96b19e06aad7c9dda857 Mon Sep 17 00:00:00 2001
From: Pratzybha <84196704+Pratzybha@users.noreply.github.com>
Date: Sun, 27 Oct 2024 09:11:57 +0530
Subject: [PATCH 3/4] Update README.md

---
 .../Model/README.md                           | 27 ++++++++-----------
 1 file changed, 11 insertions(+), 16 deletions(-)

diff --git a/Online Food Delivery Preferences/Model/README.md b/Online Food Delivery Preferences/Model/README.md
index 1370e1de0..7df776208 100644
--- a/Online Food Delivery Preferences/Model/README.md	
+++ b/Online Food Delivery Preferences/Model/README.md	
@@ -104,28 +104,23 @@ GRU (Gated Recurrent Unit): An efficient variant of LSTM, offering similar capab
 
 ### 📊 **Exploratory Data Analysis Results**
 
-<h2>Demographic  Details</h2>
-![Age Group]
-(https://github.com/Pratzybha/Images/blob/main/Images/AgeGroup.png)
+**Demographic  Details**
 
-![Demographic  Details]
-(https://github.com/Pratzybha/Images/blob/main/Images/DemographicDetails.png)
+![Age Group](https://github.com/Pratzybha/Images/blob/main/Images/AgeGroup.png)
 
-<h2>Correlation Calculation</h2>
-![Correlation Calculation]
-(https://github.com/Pratzybha/Images/blob/main/Images/CorrelationCalculation.png)
+![Demographic  Details](https://github.com/Pratzybha/Images/blob/main/Images/DemographicDetails.png)
 
-<h2>Features That Influence Users To Use Online Food Delivery</h2>
-![Features That Influence Users To Use Online Food Delivery]
-(https://github.com/Pratzybha/Images/blob/main/Images/FeaturesOnlineDelivery%201.png)
+**Correlation Calculation**
+![Correlation Calculation](https://github.com/Pratzybha/Images/blob/main/Images/CorrelationCalculation.png)
 
-<h2>Features That Discourage Users To Use Online Food Delivery</h2>
-![Features That Discourage Users To Use Online Food Delivery]
-(https://github.com/Pratzybha/Images/blob/main/Images/FeaturesOnlineDelivery%202.png)
+**Features That Influence Users To Use Online Food Delivery**
+![Features That Influence Users To Use Online Food Delivery](https://github.com/Pratzybha/Images/blob/main/Images/FeaturesOnlineDelivery%201.png)
+
+**Features That Discourage Users To Use Online Food Delivery**
+![Features That Discourage Users To Use Online Food Delivery](https://github.com/Pratzybha/Images/blob/main/Images/FeaturesOnlineDelivery%202.png)
 
 <h2>Time Factor</h2>
-![Time Factor]
-(https://github.com/Pratzybha/Images/blob/main/Images/FeaturesOnlineDelivery%203.png)
+![Time Factor](https://github.com/Pratzybha/Images/blob/main/Images/FeaturesOnlineDelivery%203.png)
 
 ### 📈 **Performance of the Models based on the Accuracy Scores**
 

From ca8534bf2c21c623a2c50fbf3b36a01a4e839527 Mon Sep 17 00:00:00 2001
From: Pratzybha <84196704+Pratzybha@users.noreply.github.com>
Date: Sun, 27 Oct 2024 09:19:56 +0530
Subject: [PATCH 4/4] Update README.md

---
 Online Food Delivery Preferences/Model/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Online Food Delivery Preferences/Model/README.md b/Online Food Delivery Preferences/Model/README.md
index 7df776208..e8e459241 100644
--- a/Online Food Delivery Preferences/Model/README.md	
+++ b/Online Food Delivery Preferences/Model/README.md	
@@ -119,7 +119,7 @@ GRU (Gated Recurrent Unit): An efficient variant of LSTM, offering similar capab
 **Features That Discourage Users To Use Online Food Delivery**
 ![Features That Discourage Users To Use Online Food Delivery](https://github.com/Pratzybha/Images/blob/main/Images/FeaturesOnlineDelivery%202.png)
 
-<h2>Time Factor</h2>
+**Time Factor**
 ![Time Factor](https://github.com/Pratzybha/Images/blob/main/Images/FeaturesOnlineDelivery%203.png)
 
 ### 📈 **Performance of the Models based on the Accuracy Scores**