final notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div align=\"center\"><h2>PHASE 5 CAPSTONE PROJECT</h2></div>\n",
    "<div align=\"center\"><h2>Flight Passenger Preferences and Booking Patterns: A Basis for Predictive Modeling at Horizon Airlines</h2></div>\n",
    "\n",
    "<div align=\"center\">\n",
    "    <div style=\"float: right;\">\n",
    "        <img src=\"heatmap_image.png\" alt=\"Heatmap Image\" width=\"500\"/>\n",
    "    </div>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TEAM\n",
    "\n",
    "* Kamande Karigi\n",
    "* Lewis Otsieka\n",
    "* Marion Jelimo\n",
    "* Titus Kilonzo Mutuku\n",
    "* Yusra Noor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BUSINESS UNDERSTANDING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## INTRODUCTION\n",
    "Horizon Airlines, a prominent player in the aviation industry, stands as a cornerstone of reliable air travel services. As a leading airline company, Horizon Airlines is dedicated to providing safe, efficient, and seamless travel experiences for passengers across Kenya. With a fleet of modern aircraft and a commitment to operational excellence, Horizon Airlines has positioned itself as a trailblazer in the aviation sector. Recognizing the challenges and complexities inherent in managing a vast network of flights, passenger preferences, and operational logistics, Horizon Airlines has enlisted our expertise. Our team has been entrusted with the task of implementing advanced analytics to derive valuable insights from the company's extensive datasets. By harnessing the power of data-driven decision-making, our goal is to assist Horizon Airlines in optimizing its operations, enhancing customer experiences, and maintaining a competitive edge in the dynamic aviation landscape.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PROBLEM STATEMENT\n",
    "In the aftermath of the COVID-19 pandemic, the aviation industry is grappling with a multifaceted challenge to revitalize its operations and regain passenger trust. The problem revolves around understanding the intricate dynamics of customer behavior post-COVID, with a focus on influencing factors such as travel preferences, safety concerns, and the demand for additional services. It is crucial for airlines to tailor their services effectively with respect to the specific needs and concerns of passengers, utilising available data to inform effective safety measures, and streamline the provision of personalised customer preferences such as extra baggage, preferred seating, and in-flight meals. This project provides an opportunity to investigate post-pandemic travel, and guide Horizon airlines to craft strategies that align with evolving customer expectations and foster a robust recovery."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## THE MAIN OBJECTIVES\n",
    "\n",
    "To develop a model that learns over the available customer booking data, whose inferences improve or smooth out Horizon airlines' services. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## THE SPECIFIC OBJECTIVES\n",
    "\n",
    "1. To improve booking conversion rates.\n",
    "2. To enhance customer segmentation by identifying distinct customer segments based on booking patterns and use the information to tailor marketing strategies, services or offers to specific groups.\n",
    "3. To increase ancilliary service adoption and optimize marketing or pricing strategies for these services.\n",
    "4. Optimize Operational Efficiency using patterns related to flight_day and flight_hour to optimize flight schedules, staffing, and other operational aspects.\n",
    "5. Improve route specific strategies.\n",
    "\n",
    "These objectives can be interconnected, and addressing one may positively impact others.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RESEARCH QUESTIONS\n",
    "\n",
    "1. Booking conversion analysis:\n",
    "What are the primary factors inluencing booking completion and how do the vary across different sales channels?\n",
    "Can the `purchase_lead` time be optimized to improve booking conversion rates without adversely affecting other aspects? \n",
    "\n",
    "The most important factor with a predictive model is the quality of the data you use to train the machine learning algorithms. For this task, you must manipulate and prepare the provided customer booking data so that you can build a high-quality predictive model.\n",
    "\n",
    "With your predictive model, it is important to interpret the results in order to understand how “predictive” the data really was and whether we can feasibly use it to predict the target outcome.\n",
    "Therefore, you should evaluate the model's performance and output how each variable contributes to the predictive model's power.\n",
    "\n",
    "2. Flight duration:\n",
    "Which features are most significant in trying to predict flight duration?\n",
    "How do aspects such as num of passengers and length of stay relate to flight duration?\n",
    "\n",
    "3. Customer segmentation:\n",
    "What are the distinct customer segments based on booking patterns, and how do these segments differ in terms of preferences and behaviors?\n",
    "How can personalized marketing strategies be developed for each identified segment?\n",
    "\n",
    "4. Ancillary Service Adoption:\n",
    "What factors drive customers to opt for extra baggage, preferred seats, or in-flight meals, and are there cross-correlations between these preferences?\n",
    "Can targeted promotions or bundling strategies be implemented to increase the adoption of these services?\n",
    "\n",
    "5. Operational Efficiency Optimization:\n",
    "How do `flight_day` and f`light_hour` affect booking patterns, and how can flight schedules be optimized to match demand?\n",
    "Are there specific routes (`route`) that exhibit distinct patterns in terms of demand and booking behavior?\n",
    "\n",
    "6. Route-specific Strategies:\n",
    "How do customer preferences and behaviors vary across different flight routes, and what marketing or pricing strategies can be implemented for each route?\n",
    "Are there external factors, such as events or seasons, that significantly impact booking patterns on specific routes?\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DEFINING METRICS OF SUCCESS\n",
    "1. Booking Conversion Rates: \n",
    "Metric: Percentage of completed bookings (`booking_complete`).\n",
    "Success Criteria: Increase in overall booking conversion rates, with a target improvement percentage.\n",
    "\n",
    "2. Flight Duration Prediction:\n",
    "Metric: Mean Absolute Error (MAE) or Root Mean Squared Error (RMSE) in predicting `flight_duration`.\n",
    "Success Criteria: Decrease in prediction error, indicating more accurate flight duration predictions.\n",
    "\n",
    "3. Customer Segmentation:\n",
    "Metric: Silhouette Score or Homogeneity Score for clustering quality.\n",
    "Success Criteria: Improved cluster separation and cohesion, indicating well-defined customer segments.\n",
    "\n",
    "4. Ancillary Service Adoption:\n",
    "Metric: Percentage of customers opting for extra baggage, preferred seats, or in-flight meals.\n",
    "Success Criteria: Increase in adoption rates for targeted ancillary services.\n",
    "\n",
    "5. Operational Efficiency Optimization:\n",
    "Metric: Percentage of flights scheduled during peak demand days.\n",
    "Success Criteria: Optimization of flight schedules with an increase in the proportion of flights scheduled during peak demand times.\n",
    "\n",
    "6. Route-specific Strategies:\n",
    "Metric: Percentage increase in bookings for specific routes.\n",
    "Success Criteria: Successful implementation of targeted marketing or pricing strategies leading to increased bookings on selected routes. \n",
    "\n",
    "8. Customer Experience Enhancement:\n",
    "Metric: Customer Satisfaction Score (CSAT) based on post-travel surveys.\n",
    "Success Criteria: Increase in overall CSAT scores, indicating enhanced customer experience.\n",
    "\n",
    "Each metric aligns with the specific objectives and is measurable over time. Feedback will be used to adjust strategies or models as needed to continuously improve performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DATA UNDERSTANDING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset consists of 50,000 entries and comprises 15 columns. It includes both numerical and categorical features, providing insights into flight passenger preferences and booking patterns. Below is an overview of the dataset columns:\n",
    "\n",
    "* num_passengers - Represents the number of passengers for each booking.\n",
    "\n",
    "* sales_channel - Denotes the specific channel through which flight bookings were made.\n",
    "\n",
    "* trip_type - Indicates the type of trip, distinguishing between one-way and round-trip bookings.\n",
    "\n",
    "* purchase_lead - Represents the lead time (in days) between considering and making a flight purchase.\n",
    "\n",
    "* length_of_stay - Captures the duration of the stay associated with each flight booking.\n",
    "\n",
    "* flight_hour - Specifies the hour of the day at which the flight is scheduled.\n",
    "\n",
    "* flight_day - Records the date of the flight booking.\n",
    "\n",
    "* route - Describes the specific route of the flight.\n",
    "\n",
    "* booking_origin - Indicates the origin of the booking, providing insight into the geographic source.\n",
    "\n",
    "* wants_extra_baggage - Binary variable indicating whether passengers express a desire for additional baggage.\n",
    "\n",
    "* wants_preferred_seat - Binary variable indicating whether passengers express a preference for specific seating.\n",
    "\n",
    "* wants_in_flight_meals - Binary variable indicating whether passengers express a desire for in-flight meals.\n",
    "\n",
    "* flight_duration - Captures the total duration of the flight for each booking.\n",
    "\n",
    "* booking_complete - Binary variable indicating whether the flight booking process is completed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DATA PROCESSING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Libraries imported successfully!\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import roc_curve, auc, precision_recall_curve\n",
    "\n",
    "# Machine Learning libraries\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "\n",
    "\n",
    "# Set a seed for reproducibility\n",
    "np.random.seed(42)\n",
    "\n",
    "# Display settings\n",
    "pd.set_option('display.max_columns', None)\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "print(\"Libraries imported successfully!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_passengers</th>\n",
       "      <th>sales_channel</th>\n",
       "      <th>trip_type</th>\n",
       "      <th>purchase_lead</th>\n",
       "      <th>length_of_stay</th>\n",
       "      <th>flight_hour</th>\n",
       "      <th>flight_day</th>\n",
       "      <th>route</th>\n",
       "      <th>booking_origin</th>\n",
       "      <th>wants_extra_baggage</th>\n",
       "      <th>wants_preferred_seat</th>\n",
       "      <th>wants_in_flight_meals</th>\n",
       "      <th>flight_duration</th>\n",
       "      <th>booking_complete</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>262</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>Sat</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>112</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>Sat</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>243</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>Wed</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>96</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>Sat</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>68</td>\n",
       "      <td>22</td>\n",
       "      <td>15</td>\n",
       "      <td>Wed</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_passengers sales_channel  trip_type  purchase_lead  length_of_stay  \\\n",
       "0               2      Internet  RoundTrip            262              19   \n",
       "1               1      Internet  RoundTrip            112              20   \n",
       "2               2      Internet  RoundTrip            243              22   \n",
       "3               1      Internet  RoundTrip             96              31   \n",
       "4               2      Internet  RoundTrip             68              22   \n",
       "\n",
       "   flight_hour flight_day   route booking_origin  wants_extra_baggage  \\\n",
       "0            7        Sat  AKLDEL    New Zealand                    1   \n",
       "1            3        Sat  AKLDEL    New Zealand                    0   \n",
       "2           17        Wed  AKLDEL          India                    1   \n",
       "3            4        Sat  AKLDEL    New Zealand                    0   \n",
       "4           15        Wed  AKLDEL          India                    1   \n",
       "\n",
       "   wants_preferred_seat  wants_in_flight_meals  flight_duration  \\\n",
       "0                     0                      0             5.52   \n",
       "1                     0                      0             5.52   \n",
       "2                     1                      0             5.52   \n",
       "3                     0                      1             5.52   \n",
       "4                     0                      1             5.52   \n",
       "\n",
       "   booking_complete  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 0  \n",
       "4                 0  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('customer_booking.csv', encoding='latin-1')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50000 entries, 0 to 49999\n",
      "Data columns (total 14 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   num_passengers         50000 non-null  int64  \n",
      " 1   sales_channel          50000 non-null  object \n",
      " 2   trip_type              50000 non-null  object \n",
      " 3   purchase_lead          50000 non-null  int64  \n",
      " 4   length_of_stay         50000 non-null  int64  \n",
      " 5   flight_hour            50000 non-null  int64  \n",
      " 6   flight_day             50000 non-null  object \n",
      " 7   route                  50000 non-null  object \n",
      " 8   booking_origin         50000 non-null  object \n",
      " 9   wants_extra_baggage    50000 non-null  int64  \n",
      " 10  wants_preferred_seat   50000 non-null  int64  \n",
      " 11  wants_in_flight_meals  50000 non-null  int64  \n",
      " 12  flight_duration        50000 non-null  float64\n",
      " 13  booking_complete       50000 non-null  int64  \n",
      "dtypes: float64(1), int64(8), object(5)\n",
      "memory usage: 5.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The DataFrame has 50,000 rows and 14 columns. There are no missing values in our data. There are 8 integer columns for numerical values, 5 object columns for categorical variables and 1 floating point column. We start by investigating the validity of the data types with respect to column values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Solarize_Light2', '_classic_test_patch', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark', 'seaborn-dark-palette', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'tableau-colorblind10']\n"
     ]
    }
   ],
   "source": [
    "print(plt.style.available)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use(\"seaborn-muted\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the distribution of numerical variables\n",
    "df.hist(figsize=(10, 8))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the visualisation, the `num_passengers` column shows that the minimum number of passengers in a booking is 1, while the maximum is 9. It also shows that the majority of bookings involve a relatively small number of passengers, with occasional bookings involving larger passenger groups. The `purchase_lead` column shows that the minimum purchase lead time is 0 days, indicating bookings made on the same day as the travel date. The maximum purchase lead time is 867 days. This suggests a varied distribution of purchase lead times, with a wide range from last-minute bookings to bookings made several months in advance.\n",
    "The columns `wants_extra_baggage`, `wants_preferred_seat`, `wants_in_flight_meals`, and `booking_complete` are distributed into 2 specific integer values; 0 and 1, indicating that they are of boolean datatype and not integer, therefore, they shall be converted to boolean to make them categorical columns.\n",
    "The `flight_hour` column shows that most flights happen early in the morning from 7:30 to 10 am. Outside of these hours, from midnight to 3pm, we have an almost even distribution of flights. These taper off as the day winds down. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wants_extra_baggage</th>\n",
       "      <th>wants_preferred_seat</th>\n",
       "      <th>wants_in_flight_meals</th>\n",
       "      <th>booking_complete</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       wants_extra_baggage  wants_preferred_seat  wants_in_flight_meals  \\\n",
       "0                     True                 False                  False   \n",
       "1                    False                 False                  False   \n",
       "2                     True                  True                  False   \n",
       "3                    False                 False                   True   \n",
       "4                     True                 False                   True   \n",
       "...                    ...                   ...                    ...   \n",
       "49995                 True                 False                   True   \n",
       "49996                False                 False                  False   \n",
       "49997                False                 False                   True   \n",
       "49998                 True                 False                   True   \n",
       "49999                False                  True                  False   \n",
       "\n",
       "       booking_complete  \n",
       "0                 False  \n",
       "1                 False  \n",
       "2                 False  \n",
       "3                 False  \n",
       "4                 False  \n",
       "...                 ...  \n",
       "49995             False  \n",
       "49996             False  \n",
       "49997             False  \n",
       "49998             False  \n",
       "49999             False  \n",
       "\n",
       "[50000 rows x 4 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool_cols = ['wants_extra_baggage', 'wants_preferred_seat', 'wants_in_flight_meals', 'booking_complete']\n",
    "df[bool_cols].astype('bool')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Selecting only binary preference variables for distribution analysis\n",
    "binary_preferences = df[['wants_extra_baggage', 'wants_preferred_seat', 'wants_in_flight_meals', 'booking_complete']]\n",
    "\n",
    "# Create subplots for each binary preference variable\n",
    "plt.figure(figsize=(14, 6))\n",
    "for i, column in enumerate(binary_preferences.columns, 1):\n",
    "    plt.subplot(1, 4, i)\n",
    "    sns.countplot(x=column, data=df)\n",
    "    plt.title(f'Distribution of {column}')\n",
    "    plt.xlabel(column)\n",
    "    plt.ylabel('Count')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "wants_extra_baggage      66.878\n",
       "wants_preferred_seat     29.696\n",
       "wants_in_flight_meals    42.714\n",
       "booking_complete         14.956\n",
       "dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binary_preferences = df[['wants_extra_baggage', 'wants_preferred_seat', 'wants_in_flight_meals', 'booking_complete']]\n",
    "percentages = binary_preferences.mean() *  100\n",
    "percentages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Approximately 67% of passengers prefer extra baggage, this is also clear from the visualization. 30% of the passengers had seats they preferred. 42% of the passengers preferred meals in flight. Only about 15% of the bookings were completed.\n",
    "After evaluating the numerical columns, we investigte categorical columns of object dtype."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'booking_origin': 104, 'trip_type': 3, 'flight_day': 7, 'route': 799}"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_columns = ['booking_origin', 'trip_type', 'flight_day', 'route']\n",
    "unique_counts = {col: df[col].nunique() for col in categorical_columns}\n",
    "unique_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define the order of days of the week starting from Monday\n",
    "ordered_days_of_week = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']\n",
    "\n",
    "# Reorder the 'flight_day' column based on the order of days of the week\n",
    "df['flight_day'] = pd.Categorical(df['flight_day'], categories=ordered_days_of_week, ordered=True)\n",
    "\n",
    "plt.figure(figsize=(18,  5))\n",
    "\n",
    "# Plot the distribution of 'trip_type' separately\n",
    "plt.subplot(1,  3,  1)\n",
    "sns.countplot(x='trip_type', data=df)\n",
    "plt.title('Distribution of Trip Type')\n",
    "plt.xlabel('Trip Type')\n",
    "plt.ylabel('Count')\n",
    "\n",
    "# Plot the distribution of 'flight_day' based on counts\n",
    "plt.subplot(1,  3,  2)\n",
    "sns.countplot(x='flight_day', data=df, order=ordered_days_of_week)\n",
    "plt.title('Distribution of Flight Day (Count)')\n",
    "plt.xlabel('Flight Day')\n",
    "plt.ylabel('Count')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Sat', 'Wed', 'Thu', 'Mon', 'Sun', 'Tue', 'Fri']\n",
       "Categories (7, object): ['Mon' < 'Tue' < 'Wed' < 'Thu' < 'Fri' < 'Sat' < 'Sun']"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['flight_day'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the chart, the highest number of bookings were round trips, followed by one way trips. Circle trips which are like round-trips, except that the route on the return trip differs from the route on the outgoing trip, were least booked.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Descriptive Statistics:\n",
      "       num_passengers  purchase_lead  length_of_stay  flight_hour  \\\n",
      "count    50000.000000   50000.000000     50000.00000  50000.00000   \n",
      "mean         1.591240      84.940480        23.04456      9.06634   \n",
      "std          1.020165      90.451378        33.88767      5.41266   \n",
      "min          1.000000       0.000000         0.00000      0.00000   \n",
      "25%          1.000000      21.000000         5.00000      5.00000   \n",
      "50%          1.000000      51.000000        17.00000      9.00000   \n",
      "75%          2.000000     115.000000        28.00000     13.00000   \n",
      "max          9.000000     867.000000       778.00000     23.00000   \n",
      "\n",
      "       wants_extra_baggage  wants_preferred_seat  wants_in_flight_meals  \\\n",
      "count         50000.000000          50000.000000           50000.000000   \n",
      "mean              0.668780              0.296960               0.427140   \n",
      "std               0.470657              0.456923               0.494668   \n",
      "min               0.000000              0.000000               0.000000   \n",
      "25%               0.000000              0.000000               0.000000   \n",
      "50%               1.000000              0.000000               0.000000   \n",
      "75%               1.000000              1.000000               1.000000   \n",
      "max               1.000000              1.000000               1.000000   \n",
      "\n",
      "       flight_duration  booking_complete  \n",
      "count     50000.000000      50000.000000  \n",
      "mean          7.277561          0.149560  \n",
      "std           1.496863          0.356643  \n",
      "min           4.670000          0.000000  \n",
      "25%           5.620000          0.000000  \n",
      "50%           7.570000          0.000000  \n",
      "75%           8.830000          0.000000  \n",
      "max           9.500000          1.000000  \n"
     ]
    }
   ],
   "source": [
    "# Descriptive Statistics\n",
    "print(\"\\nDescriptive Statistics:\")\n",
    "print(df.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data Distribution - Histograms\n",
    "numerical_columns = ['num_passengers', 'purchase_lead', 'length_of_stay', 'flight_hour', 'flight_duration']\n",
    "df[numerical_columns].hist(bins=20, figsize=(15, 10))\n",
    "plt.suptitle(\"Data Distribution - Histograms\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data Distribution - Categorical Variables\n",
    "categorical_columns = ['sales_channel']\n",
    "for column in categorical_columns:\n",
    "    sns.countplot(x=column, data=df)\n",
    "    plt.title(f'Distribution of {column}')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore Relationships - Example: Sales Channel vs. Booking Complete\n",
    "sns.countplot(x='sales_channel', hue='booking_complete', data=df)\n",
    "plt.title(\"Relationship between Sales Channel and Booking Complete\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pattern Recognition - Example: Sales Channel vs. Num Passengers\n",
    "sns.boxplot(x='sales_channel', y='num_passengers', data=df)\n",
    "plt.title(\"Sales Channel vs. Num Passengers\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Segmentation - Example: Trip Type vs. Flight Duration\n",
    "sns.barplot(x='trip_type', y='flight_duration', data=df)\n",
    "plt.title(\"Trip Type vs. Flight Duration\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Outlier Detection - Example: Boxplot for Purchase Lead\n",
    "sns.boxplot(x='purchase_lead', data=df)\n",
    "plt.title(\"Boxplot for Purchase Lead\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the top 10 routes\n",
    "top_routes = df['route'].value_counts().nlargest(10)\n",
    "\n",
    "# Plot the bar graph\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.barplot(x=top_routes.index, y=top_routes.values, palette='viridis')\n",
    "plt.title('Top 10 Routes with the Highest Number of Bookings')\n",
    "plt.xlabel('Route')\n",
    "plt.ylabel('Number of Bookings')\n",
    "plt.xticks(rotation=45, ha='right')  # Rotate x-axis labels for better readability\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the chart, the highest number of bookings were round trips, followed by one way trips. Circle trips which are like round-trips, except that the route on the return trip differs from the route on the outgoing trip, were least booked.\n",
    "\n",
    "Routes like \"AKLKUL,\" \"PENTPE,\" and \"MELSGN\" seem to be popular, as they have the highest number of bookings. This could indicate high demand or frequent travel between these locations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the top 10 booking origins\n",
    "top_booking_origins = df['booking_origin'].value_counts().nlargest(10)\n",
    "\n",
    "# Plot the bar graph\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.barplot(x=top_booking_origins.index, y=top_booking_origins.values, palette='viridis')\n",
    "plt.title('Top 10 Booking Origins with the Highest Number of Bookings')\n",
    "plt.xlabel('Booking Origin')\n",
    "plt.ylabel('Number of Bookings')\n",
    "plt.xticks(rotation=45, ha='right')  # Rotate x-axis labels for better readability\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Australia stands out as the top booking origin, indicating a significant number of travelers originating from or booking flights to Australia. Malaysia and South Korea follow Australia in the ranking. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pattern Recognition - Example: Sales Channel vs. Num Passengers\n",
    "sns.boxplot(x='sales_channel', y='num_passengers', data=df)\n",
    "plt.title(\"Sales Channel vs. Num Passengers\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def corr_matrix(df):\n",
    "    # Select only numeric columns\n",
    "    numeric_df = df.select_dtypes(include=[np.number])\n",
    "    \n",
    "    # Compute the correlation matrix\n",
    "    correlation_matrix = numeric_df.corr()\n",
    "    \n",
    "    return correlation_matrix\n",
    "\n",
    "correlation_matrix = corr_matrix(df)\n",
    "\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
    "plt.title(\"Correlation Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Given `flight_duration` is in hours, we convert `purchase_lead` and `length_of_stay` into hours too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   num_passengers sales_channel  trip_type  flight_hour flight_day   route  \\\n",
      "0               2      Internet  RoundTrip            7        Sat  AKLDEL   \n",
      "1               1      Internet  RoundTrip            3        Sat  AKLDEL   \n",
      "2               2      Internet  RoundTrip           17        Wed  AKLDEL   \n",
      "3               1      Internet  RoundTrip            4        Sat  AKLDEL   \n",
      "4               2      Internet  RoundTrip           15        Wed  AKLDEL   \n",
      "\n",
      "  booking_origin  wants_extra_baggage  wants_preferred_seat  \\\n",
      "0    New Zealand                    1                     0   \n",
      "1    New Zealand                    0                     0   \n",
      "2          India                    1                     1   \n",
      "3    New Zealand                    0                     0   \n",
      "4          India                    1                     0   \n",
      "\n",
      "   wants_in_flight_meals  flight_duration  booking_complete  \\\n",
      "0                      0             5.52                 0   \n",
      "1                      0             5.52                 0   \n",
      "2                      0             5.52                 0   \n",
      "3                      1             5.52                 0   \n",
      "4                      1             5.52                 0   \n",
      "\n",
      "   purchase_lead_hours  length_of_stay_hours  \n",
      "0                 6288                   456  \n",
      "1                 2688                   480  \n",
      "2                 5832                   528  \n",
      "3                 2304                   744  \n",
      "4                 1632                   528  \n"
     ]
    }
   ],
   "source": [
    "# Define conversion factors\n",
    "hours_in_day = 24\n",
    "\n",
    "# Convert time-related columns to hours\n",
    "df['purchase_lead_hours'] = df['purchase_lead'] * hours_in_day\n",
    "df['length_of_stay_hours'] = df['length_of_stay'] * hours_in_day\n",
    "\n",
    "# Drop the original columns\n",
    "df.drop(columns=['purchase_lead', 'length_of_stay'], inplace=True)\n",
    "\n",
    "# Print first few rows to verify the changes\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We encode flight_day as integers representing the 7 days of the week. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      flight_day flight_day_encoded\n",
      "33553        Thu                4.0\n",
      "9427         Mon                1.0\n",
      "199          Mon                1.0\n",
      "12447        Mon                1.0\n",
      "39489        Thu                4.0\n",
      "42724        Wed                3.0\n"
     ]
    }
   ],
   "source": [
    "# Define the mapping of days to integers\n",
    "day_mapping = {'Mon': 1.0, 'Tue': 2.0, 'Wed': 3.0, 'Thu': 4.0, 'Fri': 5.0, 'Sat': 6.0, 'Sun': 7.0}\n",
    "\n",
    "# Map the 'flight_day' column using the day_mapping dictionary\n",
    "df['flight_day_encoded'] = df['flight_day'].map(day_mapping)\n",
    "\n",
    "# Verify that the encoding matches the defined mapping\n",
    "print(df[['flight_day', 'flight_day_encoded']].sample(6))\n",
    "# Create 'is_weekend_flight' feature based on 'flight_day'\n",
    "\n",
    "df['is_weekend_flight'] = df['flight_day_encoded'].apply(lambda x:  1 if x >=  6 else  0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Binning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "def categorize_time_of_day(time):\n",
    "    hour = int(time.split(':')[0])\n",
    "    if 6 <= hour < 12:\n",
    "        return 'Morning'\n",
    "    elif 12 <= hour < 18:\n",
    "        return 'Afternoon'\n",
    "    else:\n",
    "        return 'Evening'\n",
    "\n",
    "# Convert integer values to string representations of time\n",
    "df['flight_hour_str'] = df['flight_hour'].astype(str)\n",
    "\n",
    "# Apply categorize_time_of_day function\n",
    "df['time_of_day_preference'] = df['flight_hour_str'].apply(categorize_time_of_day)\n",
    "\n",
    "# Drop after use\n",
    "df.drop(columns=['flight_hour_str'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create buckets for 'length_of_stay_hours', 'flight_duration' and 'purchase_lead_hours' to categorize stays into short, medium, long, etc. This can help simplify the model and potentially reveal patterns related to stay durations.\n",
    "\n",
    "Also create buckets based on time period such as weeks and months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   num_passengers sales_channel  trip_type  flight_hour flight_day   route  \\\n",
      "0               2      Internet  RoundTrip            7        Sat  AKLDEL   \n",
      "1               1      Internet  RoundTrip            3        Sat  AKLDEL   \n",
      "2               2      Internet  RoundTrip           17        Wed  AKLDEL   \n",
      "3               1      Internet  RoundTrip            4        Sat  AKLDEL   \n",
      "4               2      Internet  RoundTrip           15        Wed  AKLDEL   \n",
      "\n",
      "  booking_origin  wants_extra_baggage  wants_preferred_seat  \\\n",
      "0    New Zealand                    1                     0   \n",
      "1    New Zealand                    0                     0   \n",
      "2          India                    1                     1   \n",
      "3    New Zealand                    0                     0   \n",
      "4          India                    1                     0   \n",
      "\n",
      "   wants_in_flight_meals  flight_duration  booking_complete  \\\n",
      "0                      0             5.52                 0   \n",
      "1                      0             5.52                 0   \n",
      "2                      0             5.52                 0   \n",
      "3                      1             5.52                 0   \n",
      "4                      1             5.52                 0   \n",
      "\n",
      "   purchase_lead_hours  length_of_stay_hours flight_day_encoded  \\\n",
      "0                 6288                   456                6.0   \n",
      "1                 2688                   480                6.0   \n",
      "2                 5832                   528                3.0   \n",
      "3                 2304                   744                6.0   \n",
      "4                 1632                   528                3.0   \n",
      "\n",
      "   is_weekend_flight time_of_day_preference length_of_stay_hours_binned  \\\n",
      "0                  1                Morning                      Medium   \n",
      "1                  1                Evening                      Medium   \n",
      "2                  0              Afternoon                      Medium   \n",
      "3                  1                Evening                        Long   \n",
      "4                  0              Afternoon                      Medium   \n",
      "\n",
      "  purchase_lead_hours_binned flight_duration_binned  \n",
      "0                       Long                  Short  \n",
      "1                       Long                  Short  \n",
      "2                       Long                  Short  \n",
      "3                       Long                  Short  \n",
      "4                     Medium                  Short  \n"
     ]
    }
   ],
   "source": [
    "def bin_column(data, num_bins, labels):\n",
    "    # Use pd.qcut() to bin the column\n",
    "    binned_column = pd.qcut(data, q=num_bins, labels=labels)\n",
    "    return binned_column\n",
    "\n",
    "# define quantiles\n",
    "num_bins =  3\n",
    "labels = ['Short', 'Medium', 'Long']\n",
    "column_= ['length_of_stay_hours', 'purchase_lead_hours', 'flight_duration']\n",
    "\n",
    "# Create new binned columns for each duration column\n",
    "for col in column_:\n",
    "    # Apply bin_column function to create binned column\n",
    "    df[col + '_binned'] = bin_column(df[col], num_bins, labels)\n",
    "\n",
    "# Display the DataFrame with new binned columns\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Deriving more insightful features from existing features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create interaction features by combining related variables. For example, we can create features like \"wants_extra_baggage_and_preferred_seat\" or \"wants_meals_and_preferred_seat\" to capture combinations of customer preferences. simply multiply the boolean columns representing individual preferences. This will result in a new binary column where a value of 1 indicates that both preferences are true, and a value of 0 indicates that at least one preference is false."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create new features for combinations of customer preferences\n",
    "df['wants_extra_baggage_and_preferred_seat'] = df['wants_extra_baggage'] & df['wants_preferred_seat']\n",
    "df['wants_meals_and_preferred_seat'] = df['wants_in_flight_meals'] & df['wants_preferred_seat']\n",
    "df['wants_extra_baggage_and_meals'] = df['wants_extra_baggage'] & df['wants_in_flight_meals']\n",
    "df['wants_all_services'] = df['wants_extra_baggage'] & df['wants_preferred_seat'] & df['wants_in_flight_meals']\n",
    "df['wants_seat_and_meals_but_no_baggage'] = (df['wants_preferred_seat'] & df['wants_in_flight_meals']) & ~df['wants_extra_baggage']\n",
    "df['wants_baggage_and_meals_but_no_seat'] = (df['wants_extra_baggage'] & df['wants_in_flight_meals']) & ~df['wants_preferred_seat']\n",
    "df['wants_baggage_and_seat_but_no_meals'] = (df['wants_extra_baggage'] & df['wants_preferred_seat']) & ~df['wants_in_flight_meals']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Columns building using aggregate statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_passengers</th>\n",
       "      <th>sales_channel</th>\n",
       "      <th>trip_type</th>\n",
       "      <th>flight_hour</th>\n",
       "      <th>flight_day</th>\n",
       "      <th>route</th>\n",
       "      <th>booking_origin</th>\n",
       "      <th>wants_extra_baggage</th>\n",
       "      <th>wants_preferred_seat</th>\n",
       "      <th>wants_in_flight_meals</th>\n",
       "      <th>flight_duration</th>\n",
       "      <th>booking_complete</th>\n",
       "      <th>purchase_lead_hours</th>\n",
       "      <th>length_of_stay_hours</th>\n",
       "      <th>flight_day_encoded</th>\n",
       "      <th>is_weekend_flight</th>\n",
       "      <th>time_of_day_preference</th>\n",
       "      <th>length_of_stay_hours_binned</th>\n",
       "      <th>purchase_lead_hours_binned</th>\n",
       "      <th>flight_duration_binned</th>\n",
       "      <th>wants_extra_baggage_and_preferred_seat</th>\n",
       "      <th>wants_meals_and_preferred_seat</th>\n",
       "      <th>wants_extra_baggage_and_meals</th>\n",
       "      <th>wants_all_services</th>\n",
       "      <th>wants_seat_and_meals_but_no_baggage</th>\n",
       "      <th>wants_baggage_and_meals_but_no_seat</th>\n",
       "      <th>wants_baggage_and_seat_but_no_meals</th>\n",
       "      <th>total_preferences</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>Wed</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>4</td>\n",
       "      <td>Sat</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>15</td>\n",
       "      <td>Wed</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1632</td>\n",
       "      <td>528</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Short</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_passengers sales_channel  trip_type  flight_hour flight_day   route  \\\n",
       "0               2      Internet  RoundTrip            7        Sat  AKLDEL   \n",
       "1               1      Internet  RoundTrip            3        Sat  AKLDEL   \n",
       "2               2      Internet  RoundTrip           17        Wed  AKLDEL   \n",
       "3               1      Internet  RoundTrip            4        Sat  AKLDEL   \n",
       "4               2      Internet  RoundTrip           15        Wed  AKLDEL   \n",
       "\n",
       "  booking_origin  wants_extra_baggage  wants_preferred_seat  \\\n",
       "0    New Zealand                    1                     0   \n",
       "1    New Zealand                    0                     0   \n",
       "2          India                    1                     1   \n",
       "3    New Zealand                    0                     0   \n",
       "4          India                    1                     0   \n",
       "\n",
       "   wants_in_flight_meals  flight_duration  booking_complete  \\\n",
       "0                      0             5.52                 0   \n",
       "1                      0             5.52                 0   \n",
       "2                      0             5.52                 0   \n",
       "3                      1             5.52                 0   \n",
       "4                      1             5.52                 0   \n",
       "\n",
       "   purchase_lead_hours  length_of_stay_hours flight_day_encoded  \\\n",
       "0                 6288                   456                6.0   \n",
       "1                 2688                   480                6.0   \n",
       "2                 5832                   528                3.0   \n",
       "3                 2304                   744                6.0   \n",
       "4                 1632                   528                3.0   \n",
       "\n",
       "   is_weekend_flight time_of_day_preference length_of_stay_hours_binned  \\\n",
       "0                  1                Morning                      Medium   \n",
       "1                  1                Evening                      Medium   \n",
       "2                  0              Afternoon                      Medium   \n",
       "3                  1                Evening                        Long   \n",
       "4                  0              Afternoon                      Medium   \n",
       "\n",
       "  purchase_lead_hours_binned flight_duration_binned  \\\n",
       "0                       Long                  Short   \n",
       "1                       Long                  Short   \n",
       "2                       Long                  Short   \n",
       "3                       Long                  Short   \n",
       "4                     Medium                  Short   \n",
       "\n",
       "   wants_extra_baggage_and_preferred_seat  wants_meals_and_preferred_seat  \\\n",
       "0                                       0                               0   \n",
       "1                                       0                               0   \n",
       "2                                       1                               0   \n",
       "3                                       0                               0   \n",
       "4                                       0                               0   \n",
       "\n",
       "   wants_extra_baggage_and_meals  wants_all_services  \\\n",
       "0                              0                   0   \n",
       "1                              0                   0   \n",
       "2                              0                   0   \n",
       "3                              0                   0   \n",
       "4                              1                   0   \n",
       "\n",
       "   wants_seat_and_meals_but_no_baggage  wants_baggage_and_meals_but_no_seat  \\\n",
       "0                                    0                                    0   \n",
       "1                                    0                                    0   \n",
       "2                                    0                                    0   \n",
       "3                                    0                                    0   \n",
       "4                                    0                                    1   \n",
       "\n",
       "   wants_baggage_and_seat_but_no_meals  total_preferences  \n",
       "0                                    0                  1  \n",
       "1                                    0                  0  \n",
       "2                                    1                  2  \n",
       "3                                    0                  1  \n",
       "4                                    0                  2  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[bool_cols] = df[bool_cols].astype('int')\n",
    "\n",
    "df['total_preferences'] = df['wants_extra_baggage'] + df['wants_preferred_seat'] + df['wants_in_flight_meals']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>total_preferences</th>\n",
       "      <th>avg_purchase_lead_sales</th>\n",
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       "      <td>3</td>\n",
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       "      <td>AKLDEL</td>\n",
       "      <td>New Zealand</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>17</td>\n",
       "      <td>Wed</td>\n",
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       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>4</td>\n",
       "      <td>Sat</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "      <td>2304</td>\n",
       "      <td>744</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Evening</td>\n",
       "      <td>Long</td>\n",
       "      <td>Long</td>\n",
       "      <td>Short</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>15</td>\n",
       "      <td>Wed</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "      <td>1632</td>\n",
       "      <td>528</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Short</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_passengers sales_channel  trip_type  flight_hour flight_day   route  \\\n",
       "0               2      Internet  RoundTrip            7        Sat  AKLDEL   \n",
       "1               1      Internet  RoundTrip            3        Sat  AKLDEL   \n",
       "2               2      Internet  RoundTrip           17        Wed  AKLDEL   \n",
       "3               1      Internet  RoundTrip            4        Sat  AKLDEL   \n",
       "4               2      Internet  RoundTrip           15        Wed  AKLDEL   \n",
       "\n",
       "  booking_origin  wants_extra_baggage  wants_preferred_seat  \\\n",
       "0    New Zealand                    1                     0   \n",
       "1    New Zealand                    0                     0   \n",
       "2          India                    1                     1   \n",
       "3    New Zealand                    0                     0   \n",
       "4          India                    1                     0   \n",
       "\n",
       "   wants_in_flight_meals  flight_duration  booking_complete  \\\n",
       "0                      0             5.52                 0   \n",
       "1                      0             5.52                 0   \n",
       "2                      0             5.52                 0   \n",
       "3                      1             5.52                 0   \n",
       "4                      1             5.52                 0   \n",
       "\n",
       "   purchase_lead_hours  length_of_stay_hours flight_day_encoded  \\\n",
       "0                 6288                   456                6.0   \n",
       "1                 2688                   480                6.0   \n",
       "2                 5832                   528                3.0   \n",
       "3                 2304                   744                6.0   \n",
       "4                 1632                   528                3.0   \n",
       "\n",
       "   is_weekend_flight time_of_day_preference length_of_stay_hours_binned  \\\n",
       "0                  1                Morning                      Medium   \n",
       "1                  1                Evening                      Medium   \n",
       "2                  0              Afternoon                      Medium   \n",
       "3                  1                Evening                        Long   \n",
       "4                  0              Afternoon                      Medium   \n",
       "\n",
       "  purchase_lead_hours_binned flight_duration_binned  \\\n",
       "0                       Long                  Short   \n",
       "1                       Long                  Short   \n",
       "2                       Long                  Short   \n",
       "3                       Long                  Short   \n",
       "4                     Medium                  Short   \n",
       "\n",
       "   wants_extra_baggage_and_preferred_seat  wants_meals_and_preferred_seat  \\\n",
       "0                                       0                               0   \n",
       "1                                       0                               0   \n",
       "2                                       1                               0   \n",
       "3                                       0                               0   \n",
       "4                                       0                               0   \n",
       "\n",
       "   wants_extra_baggage_and_meals  wants_all_services  \\\n",
       "0                              0                   0   \n",
       "1                              0                   0   \n",
       "2                              0                   0   \n",
       "3                              0                   0   \n",
       "4                              1                   0   \n",
       "\n",
       "   wants_seat_and_meals_but_no_baggage  wants_baggage_and_meals_but_no_seat  \\\n",
       "0                                    0                                    0   \n",
       "1                                    0                                    0   \n",
       "2                                    0                                    0   \n",
       "3                                    0                                    0   \n",
       "4                                    0                                    1   \n",
       "\n",
       "   wants_baggage_and_seat_but_no_meals  total_preferences  \\\n",
       "0                                    0                  1   \n",
       "1                                    0                  0   \n",
       "2                                    1                  2   \n",
       "3                                    0                  1   \n",
       "4                                    0                  2   \n",
       "\n",
       "   avg_purchase_lead_sales  std_purchase_lead_sales  avg_length_of_stay_sales  \\\n",
       "0              2048.920373              2173.450504                570.532919   \n",
       "1              2048.920373              2173.450504                570.532919   \n",
       "2              2048.920373              2173.450504                570.532919   \n",
       "3              2048.920373              2173.450504                570.532919   \n",
       "4              2048.920373              2173.450504                570.532919   \n",
       "\n",
       "   std_length_of_stay_sales  \n",
       "0                828.813233  \n",
       "1                828.813233  \n",
       "2                828.813233  \n",
       "3                828.813233  \n",
       "4                828.813233  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate aggregate statistics of purchase_lead and length_of_stay (mean and standard deviation) grouped by sales_channel\n",
    "agg_sales = df.groupby('sales_channel').agg({'purchase_lead_hours': ['mean', 'std'], 'length_of_stay_hours': ['mean', 'std']}).reset_index()\n",
    "\n",
    "agg_sales.columns = ['sales_channel', 'avg_purchase_lead_sales', 'std_purchase_lead_sales', 'avg_length_of_stay_sales', 'std_length_of_stay_sales']\n",
    "\n",
    "df = pd.merge(df, agg_sales, on='sales_channel', how='left')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_passengers</th>\n",
       "      <th>sales_channel</th>\n",
       "      <th>trip_type</th>\n",
       "      <th>flight_hour</th>\n",
       "      <th>flight_day</th>\n",
       "      <th>route</th>\n",
       "      <th>booking_origin</th>\n",
       "      <th>wants_extra_baggage</th>\n",
       "      <th>wants_preferred_seat</th>\n",
       "      <th>wants_in_flight_meals</th>\n",
       "      <th>flight_duration</th>\n",
       "      <th>booking_complete</th>\n",
       "      <th>purchase_lead_hours</th>\n",
       "      <th>length_of_stay_hours</th>\n",
       "      <th>flight_day_encoded</th>\n",
       "      <th>is_weekend_flight</th>\n",
       "      <th>time_of_day_preference</th>\n",
       "      <th>length_of_stay_hours_binned</th>\n",
       "      <th>purchase_lead_hours_binned</th>\n",
       "      <th>flight_duration_binned</th>\n",
       "      <th>wants_extra_baggage_and_preferred_seat</th>\n",
       "      <th>wants_meals_and_preferred_seat</th>\n",
       "      <th>wants_extra_baggage_and_meals</th>\n",
       "      <th>wants_all_services</th>\n",
       "      <th>wants_seat_and_meals_but_no_baggage</th>\n",
       "      <th>wants_baggage_and_meals_but_no_seat</th>\n",
       "      <th>wants_baggage_and_seat_but_no_meals</th>\n",
       "      <th>total_preferences</th>\n",
       "      <th>avg_purchase_lead_sales</th>\n",
       "      <th>std_purchase_lead_sales</th>\n",
       "      <th>avg_length_of_stay_sales</th>\n",
       "      <th>std_length_of_stay_sales</th>\n",
       "      <th>avg_purchase_lead_trip</th>\n",
       "      <th>std_purchase_lead_trip</th>\n",
       "      <th>avg_length_of_stay_trip</th>\n",
       "      <th>std_length_of_stay_trip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>7</td>\n",
       "      <td>Sat</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "      <td>6288</td>\n",
       "      <td>456</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Long</td>\n",
       "      <td>Short</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "      <td>2036.749379</td>\n",
       "      <td>2171.215514</td>\n",
       "      <td>553.88666</td>\n",
       "      <td>814.683874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>3</td>\n",
       "      <td>Sat</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "      <td>2688</td>\n",
       "      <td>480</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Evening</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Long</td>\n",
       "      <td>Short</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "      <td>2036.749379</td>\n",
       "      <td>2171.215514</td>\n",
       "      <td>553.88666</td>\n",
       "      <td>814.683874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>17</td>\n",
       "      <td>Wed</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "      <td>5832</td>\n",
       "      <td>528</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Long</td>\n",
       "      <td>Short</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "      <td>2036.749379</td>\n",
       "      <td>2171.215514</td>\n",
       "      <td>553.88666</td>\n",
       "      <td>814.683874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>4</td>\n",
       "      <td>Sat</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "      <td>2304</td>\n",
       "      <td>744</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Evening</td>\n",
       "      <td>Long</td>\n",
       "      <td>Long</td>\n",
       "      <td>Short</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "      <td>2036.749379</td>\n",
       "      <td>2171.215514</td>\n",
       "      <td>553.88666</td>\n",
       "      <td>814.683874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>15</td>\n",
       "      <td>Wed</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "      <td>1632</td>\n",
       "      <td>528</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Short</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "      <td>2036.749379</td>\n",
       "      <td>2171.215514</td>\n",
       "      <td>553.88666</td>\n",
       "      <td>814.683874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_passengers sales_channel  trip_type  flight_hour flight_day   route  \\\n",
       "0               2      Internet  RoundTrip            7        Sat  AKLDEL   \n",
       "1               1      Internet  RoundTrip            3        Sat  AKLDEL   \n",
       "2               2      Internet  RoundTrip           17        Wed  AKLDEL   \n",
       "3               1      Internet  RoundTrip            4        Sat  AKLDEL   \n",
       "4               2      Internet  RoundTrip           15        Wed  AKLDEL   \n",
       "\n",
       "  booking_origin  wants_extra_baggage  wants_preferred_seat  \\\n",
       "0    New Zealand                    1                     0   \n",
       "1    New Zealand                    0                     0   \n",
       "2          India                    1                     1   \n",
       "3    New Zealand                    0                     0   \n",
       "4          India                    1                     0   \n",
       "\n",
       "   wants_in_flight_meals  flight_duration  booking_complete  \\\n",
       "0                      0             5.52                 0   \n",
       "1                      0             5.52                 0   \n",
       "2                      0             5.52                 0   \n",
       "3                      1             5.52                 0   \n",
       "4                      1             5.52                 0   \n",
       "\n",
       "   purchase_lead_hours  length_of_stay_hours flight_day_encoded  \\\n",
       "0                 6288                   456                6.0   \n",
       "1                 2688                   480                6.0   \n",
       "2                 5832                   528                3.0   \n",
       "3                 2304                   744                6.0   \n",
       "4                 1632                   528                3.0   \n",
       "\n",
       "   is_weekend_flight time_of_day_preference length_of_stay_hours_binned  \\\n",
       "0                  1                Morning                      Medium   \n",
       "1                  1                Evening                      Medium   \n",
       "2                  0              Afternoon                      Medium   \n",
       "3                  1                Evening                        Long   \n",
       "4                  0              Afternoon                      Medium   \n",
       "\n",
       "  purchase_lead_hours_binned flight_duration_binned  \\\n",
       "0                       Long                  Short   \n",
       "1                       Long                  Short   \n",
       "2                       Long                  Short   \n",
       "3                       Long                  Short   \n",
       "4                     Medium                  Short   \n",
       "\n",
       "   wants_extra_baggage_and_preferred_seat  wants_meals_and_preferred_seat  \\\n",
       "0                                       0                               0   \n",
       "1                                       0                               0   \n",
       "2                                       1                               0   \n",
       "3                                       0                               0   \n",
       "4                                       0                               0   \n",
       "\n",
       "   wants_extra_baggage_and_meals  wants_all_services  \\\n",
       "0                              0                   0   \n",
       "1                              0                   0   \n",
       "2                              0                   0   \n",
       "3                              0                   0   \n",
       "4                              1                   0   \n",
       "\n",
       "   wants_seat_and_meals_but_no_baggage  wants_baggage_and_meals_but_no_seat  \\\n",
       "0                                    0                                    0   \n",
       "1                                    0                                    0   \n",
       "2                                    0                                    0   \n",
       "3                                    0                                    0   \n",
       "4                                    0                                    1   \n",
       "\n",
       "   wants_baggage_and_seat_but_no_meals  total_preferences  \\\n",
       "0                                    0                  1   \n",
       "1                                    0                  0   \n",
       "2                                    1                  2   \n",
       "3                                    0                  1   \n",
       "4                                    0                  2   \n",
       "\n",
       "   avg_purchase_lead_sales  std_purchase_lead_sales  avg_length_of_stay_sales  \\\n",
       "0              2048.920373              2173.450504                570.532919   \n",
       "1              2048.920373              2173.450504                570.532919   \n",
       "2              2048.920373              2173.450504                570.532919   \n",
       "3              2048.920373              2173.450504                570.532919   \n",
       "4              2048.920373              2173.450504                570.532919   \n",
       "\n",
       "   std_length_of_stay_sales  avg_purchase_lead_trip  std_purchase_lead_trip  \\\n",
       "0                828.813233             2036.749379             2171.215514   \n",
       "1                828.813233             2036.749379             2171.215514   \n",
       "2                828.813233             2036.749379             2171.215514   \n",
       "3                828.813233             2036.749379             2171.215514   \n",
       "4                828.813233             2036.749379             2171.215514   \n",
       "\n",
       "   avg_length_of_stay_trip  std_length_of_stay_trip  \n",
       "0                553.88666               814.683874  \n",
       "1                553.88666               814.683874  \n",
       "2                553.88666               814.683874  \n",
       "3                553.88666               814.683874  \n",
       "4                553.88666               814.683874  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate aggregate statistics of purchase_lead and length_of_stay (mean and standard deviation) grouped by trip_type\n",
    "agg_trip = df.groupby('trip_type').agg({'purchase_lead_hours': ['mean', 'std'], 'length_of_stay_hours': ['mean', 'std']}).reset_index()\n",
    "\n",
    "agg_trip.columns = ['trip_type', 'avg_purchase_lead_trip', 'std_purchase_lead_trip', 'avg_length_of_stay_trip', 'std_length_of_stay_trip']\n",
    "\n",
    "df = pd.merge(df, agg_trip, on='trip_type', how='left')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "num_passengers                               int64\n",
       "sales_channel                               object\n",
       "trip_type                                   object\n",
       "flight_hour                                  int64\n",
       "flight_day                                category\n",
       "route                                       object\n",
       "booking_origin                              object\n",
       "wants_extra_baggage                          int32\n",
       "wants_preferred_seat                         int32\n",
       "wants_in_flight_meals                        int32\n",
       "flight_duration                            float64\n",
       "booking_complete                             int32\n",
       "purchase_lead_hours                          int64\n",
       "length_of_stay_hours                         int64\n",
       "flight_day_encoded                        category\n",
       "is_weekend_flight                            int64\n",
       "time_of_day_preference                      object\n",
       "length_of_stay_hours_binned               category\n",
       "purchase_lead_hours_binned                category\n",
       "flight_duration_binned                    category\n",
       "wants_extra_baggage_and_preferred_seat       int64\n",
       "wants_meals_and_preferred_seat               int64\n",
       "wants_extra_baggage_and_meals                int64\n",
       "wants_all_services                           int64\n",
       "wants_seat_and_meals_but_no_baggage          int64\n",
       "wants_baggage_and_meals_but_no_seat          int64\n",
       "wants_baggage_and_seat_but_no_meals          int64\n",
       "total_preferences                            int32\n",
       "avg_purchase_lead_sales                    float64\n",
       "std_purchase_lead_sales                    float64\n",
       "avg_length_of_stay_sales                   float64\n",
       "std_length_of_stay_sales                   float64\n",
       "avg_purchase_lead_trip                     float64\n",
       "std_purchase_lead_trip                     float64\n",
       "avg_length_of_stay_trip                    float64\n",
       "std_length_of_stay_trip                    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the variance for 'flight_duration' within each group defined by 'route'. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group by 'route' and compute the variance of 'flight_duration'\n",
    "variance_flight_duration_by_route = df.groupby('route')['flight_duration'].var().reset_index()\n",
    "variance_flight_duration_by_route.columns = ['route', 'variance_flight_duration_by_route']\n",
    "\n",
    "# Group by 'booking_origin' and compute the variance of 'flight_duration'\n",
    "variance_flight_duration_by_origin = df.groupby('booking_origin')['flight_duration'].var().reset_index()\n",
    "variance_flight_duration_by_origin.columns = ['booking_origin', 'variance_flight_duration_by_origin']\n",
    "\n",
    "# Merge the variance results for 'route' back to the original DataFrame\n",
    "df = pd.merge(df, variance_flight_duration_by_route, on='route', how='left')\n",
    "\n",
    "# Merge the variance results for 'booking_origin' back to the original DataFrame\n",
    "df = pd.merge(df, variance_flight_duration_by_origin, on='booking_origin', how='left')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since 'flight_day_encoded' is a categorical variable we can not compute variance. Instead, we compute the mode or frequency distribution for 'flight_day_encoded' within each group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group by 'route' and compute the mode of 'flight_day_encoded'\n",
    "mode_flight_day_by_route = df.groupby('route')['flight_day_encoded'].agg(lambda x: x.mode()[0]).reset_index()\n",
    "mode_flight_day_by_route.columns = ['route', 'mode_flight_day_by_route']\n",
    "\n",
    "# Group by 'booking_origin' and compute the mode of 'flight_day_encoded'\n",
    "mode_flight_day_by_origin = df.groupby('booking_origin')['flight_day_encoded'].agg(lambda x: x.mode()[0]).reset_index()\n",
    "mode_flight_day_by_origin.columns = ['booking_origin', 'mode_flight_day_by_origin']\n",
    "\n",
    "# Merge the mode results for 'route' back to the original DataFrame\n",
    "df = pd.merge(df, mode_flight_day_by_route, on='route', how='left')\n",
    "\n",
    "# Merge the mode results for 'booking_origin' back to the original DataFrame\n",
    "df = pd.merge(df, mode_flight_day_by_origin, on='booking_origin', how='left')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "num_passengers                               int64\n",
       "sales_channel                               object\n",
       "trip_type                                   object\n",
       "flight_hour                                  int64\n",
       "flight_day                                category\n",
       "route                                       object\n",
       "booking_origin                              object\n",
       "wants_extra_baggage                          int32\n",
       "wants_preferred_seat                         int32\n",
       "wants_in_flight_meals                        int32\n",
       "flight_duration                            float64\n",
       "booking_complete                             int32\n",
       "purchase_lead_hours                          int64\n",
       "length_of_stay_hours                         int64\n",
       "flight_day_encoded                        category\n",
       "is_weekend_flight                            int64\n",
       "time_of_day_preference                      object\n",
       "length_of_stay_hours_binned               category\n",
       "purchase_lead_hours_binned                category\n",
       "flight_duration_binned                    category\n",
       "wants_extra_baggage_and_preferred_seat       int64\n",
       "wants_meals_and_preferred_seat               int64\n",
       "wants_extra_baggage_and_meals                int64\n",
       "wants_all_services                           int64\n",
       "wants_seat_and_meals_but_no_baggage          int64\n",
       "wants_baggage_and_meals_but_no_seat          int64\n",
       "wants_baggage_and_seat_but_no_meals          int64\n",
       "total_preferences                            int32\n",
       "avg_purchase_lead_sales                    float64\n",
       "std_purchase_lead_sales                    float64\n",
       "avg_length_of_stay_sales                   float64\n",
       "std_length_of_stay_sales                   float64\n",
       "avg_purchase_lead_trip                     float64\n",
       "std_purchase_lead_trip                     float64\n",
       "avg_length_of_stay_trip                    float64\n",
       "std_length_of_stay_trip                    float64\n",
       "variance_flight_duration_by_route          float64\n",
       "variance_flight_duration_by_origin         float64\n",
       "mode_flight_day_by_route                   float64\n",
       "mode_flight_day_by_origin                  float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44474    7.0\n",
       "2449     3.0\n",
       "8387     1.0\n",
       "2787     1.0\n",
       "21906    1.0\n",
       "13020    1.0\n",
       "20258    1.0\n",
       "17584    3.0\n",
       "31596    1.0\n",
       "29746    1.0\n",
       "Name: mode_flight_day_by_origin, dtype: float64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['mode_flight_day_by_origin'].sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. More airport data to help improve model inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      flight_day flight_day_encoded\n",
      "11212        Mon                1.0\n",
      "8613         Thu                4.0\n",
      "32089        Mon                1.0\n",
      "36941        Mon                1.0\n",
      "29907        Mon                1.0\n",
      "22703        Sun                7.0\n"
     ]
    }
   ],
   "source": [
    "import airportsdata\n",
    "\n",
    "# Load the airport data using the IATA code as the key\n",
    "airports = airportsdata.load('IATA')\n",
    "\n",
    "# Function to retrieve airport information for an airport code\n",
    "def get_airport_info(airport_code):\n",
    "    airport_info = airports.get(airport_code)\n",
    "    if airport_info:\n",
    "        return {\n",
    "            'latitude': airport_info['lat'],\n",
    "            'longitude': airport_info['lon'],\n",
    "            'city': airport_info['city'],\n",
    "            'timezone': airport_info['tz']\n",
    "        }\n",
    "    else:\n",
    "        return None  # Return None if data is not found for the airport code\n",
    "\n",
    "# Iterate over rows in the dataframe and retrieve airport information for each airport code\n",
    "for index, row in df.iterrows():\n",
    "    origin_code = row['route'][:3]\n",
    "    dest_code = row['route'][3:]\n",
    "    \n",
    "    origin_info = get_airport_info(origin_code)\n",
    "    dest_info = get_airport_info(dest_code)\n",
    "    \n",
    "    if origin_info:\n",
    "        df.at[index, 'origin_latitude'] = origin_info['latitude']\n",
    "        df.at[index, 'origin_longitude'] = origin_info['longitude']\n",
    "        df.at[index, 'origin_city'] = origin_info['city']\n",
    "        df.at[index, 'origin_timezone'] = origin_info['timezone']\n",
    "    \n",
    "    if dest_info:\n",
    "        df.at[index, 'dest_latitude'] = dest_info['latitude']\n",
    "        df.at[index, 'dest_longitude'] = dest_info['longitude']\n",
    "        df.at[index, 'dest_city'] = dest_info['city']\n",
    "        df.at[index, 'dest_timezone'] = dest_info['timezone']# Define the mapping of days to integers\n",
    "day_mapping = {'Mon': 1.0, 'Tue': 2.0, 'Wed': 3.0, 'Thu': 4.0, 'Fri': 5.0, 'Sat': 6.0, 'Sun': 7.0}\n",
    "\n",
    "# Map the 'flight_day' column using the day_mapping dictionary\n",
    "df['flight_day_encoded'] = df['flight_day'].map(day_mapping)\n",
    "\n",
    "# Verify that the encoding matches the defined mapping\n",
    "print(df[['flight_day', 'flight_day_encoded']].sample(6))\n",
    "# Create 'is_weekend_flight' feature based on 'flight_day'\n",
    "\n",
    "df['is_weekend_flight'] = df['flight_day_encoded'].apply(lambda x:  1 if x >=  6 else  0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have latitude and longitude, we calculate distances.\n",
    "- *Euclidean distance* is the straight-line distance between two points in a two-dimensional space, such as a Cartesian coordinate system. When applied to geographical data, it treats the Earth's surface as a flat plane, which can lead to inaccuracies, especially for long distances. \n",
    "- *Great Circle distance*, also known as orthodromic distance, is the shortest distance between two points on the surface of a sphere, measured along the surface of the sphere (i.e., the Earth). It takes into account the curvature of the Earth, representing the shortest path between two points along the surface of the Earth. Great Circle distance is more accurate for calculating distances over long distances, especially when considering travel routes on the Earth's surface."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import radians, sin, cos, sqrt, atan2\n",
    "\n",
    "# Function to calculate euclidean distance\n",
    "\n",
    "def euclidean_distance(lat1, lon1, lat2, lon2):\n",
    "    return np.sqrt((lat2 - lat1)**2 + (lon2 - lon1)**2)\n",
    "\n",
    "# Function to calculate great circle distance\n",
    "def haversine_distance(lat1, lon1, lat2, lon2):\n",
    "    R = 6371.0  # Radius of the Earth in kilometers\n",
    "\n",
    "    # Convert latitude and longitude from degrees to radians\n",
    "    lat1 = radians(lat1)\n",
    "    lon1 = radians(lon1)\n",
    "    lat2 = radians(lat2)\n",
    "    lon2 = radians(lon2)\n",
    "\n",
    "    # Calculate the change in coordinates\n",
    "    dlon = lon2 - lon1\n",
    "    dlat = lat2 - lat1\n",
    "\n",
    "    # Use Haversine formula to calculate distance\n",
    "    a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2\n",
    "    c = 2 * atan2(sqrt(a), sqrt(1 - a))\n",
    "    distance = R * c\n",
    "\n",
    "    return distance\n",
    "\n",
    "# Calculate Euclidean Distance\n",
    "df['euclidean_distance'] = euclidean_distance(df['origin_latitude'], df['origin_longitude'], \n",
    "                                               df['dest_latitude'], df['dest_longitude'])\n",
    "\n",
    "# Calculate Great Circle Distance (Haversine Distance)\n",
    "df['great_circle_distance'] = df.apply(lambda row: haversine_distance(row['origin_latitude'], row['origin_longitude'],\n",
    "                                                                     row['dest_latitude'], row['dest_longitude']),\n",
    "                                       axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               origin_city booking_origin      dest_city   route  trip_type\n",
      "17973            Kathmandu      Australia         Sydney  KTMSYD  RoundTrip\n",
      "15124               Tehran      Australia         Sydney  IKASYD  RoundTrip\n",
      "36593            New Delhi          Japan          Osaka  DELKIX  RoundTrip\n",
      "40579             Langkawi         Taiwan         Taipei  LGKTPE  RoundTrip\n",
      "13941                Seoul      Australia     Gold Coast  ICNOOL  RoundTrip\n",
      "34247               Penang          China       Shanghai  PENPVG  RoundTrip\n",
      "43882  Chitose / Tomakomai       Thailand        Bangkok  CTSDMK  RoundTrip\n",
      "8207             New Delhi          India      Melbourne  DELMEL  RoundTrip\n",
      "20954            Melbourne      Australia  Visakhapatnam  MELVTZ  RoundTrip\n",
      "42877        Kota Kinabalu    South Korea          Busan  BKIPUS  RoundTrip\n"
     ]
    }
   ],
   "source": [
    "print(df[['origin_city', 'booking_origin', 'dest_city', 'route', 'trip_type']].sample(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analyse data on the frequency of flights between origin and destination airports. This can indicate the popularity and demand for specific routes, which may affect booking completion rates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              origin_city         dest_city  flight_count\n",
      "12               Auckland      Kuala Lumpur          2680\n",
      "600                Penang            Taipei           924\n",
      "503             Melbourne  Ho Chi Minh City           842\n",
      "692                 Seoul         Singapore           801\n",
      "58                Bangkok             Osaka           744\n",
      "694                 Seoul            Sydney           695\n",
      "59                Bangkok             Perth           679\n",
      "234  Denpasar-Bali Island             Seoul           666\n",
      "54                Bangkok        Gold Coast           655\n",
      "508             Melbourne            Penang           649\n"
     ]
    }
   ],
   "source": [
    "# Count the number of flights for each unique pair of 'origin_city' and 'dest_city'\n",
    "flight_counts = df.groupby(['origin_city', 'dest_city']).size().reset_index(name='flight_count')\n",
    "\n",
    "# Display the top N most frequent flight routes\n",
    "top_flight_routes = flight_counts.nlargest(10, 'flight_count')\n",
    "print(top_flight_routes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Plot the heatmap without labels, title, or annotations\n",
    "# plt.figure(figsize=(19,  15))\n",
    "# sns.heatmap(flight_counts_pivot, mask=mask, cmap=\"inferno\", linewidths=.5, annot=False)\n",
    "\n",
    "# # Remove axis labels\n",
    "# plt.xlabel('')\n",
    "# plt.ylabel('')\n",
    "\n",
    "# # Remove ticks\n",
    "# plt.xticks([])\n",
    "# plt.yticks([])\n",
    "\n",
    "# # Save the plot as an image without any additional text\n",
    "# plt.savefig('heatmap_image.png', bbox_inches='tight', pad_inches=0)\n",
    "\n",
    "# # Display the plot\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        booking_complete  flight_duration  weighted_booking_completion_rate\n",
      "route                                                                      \n",
      "AKLKUL               567             8.83                           5006.61\n",
      "PENTPE               401             4.67                           1872.67\n",
      "DMKKIX               187             7.00                           1309.00\n",
      "MELPEN               137             8.83                           1209.71\n",
      "ICNPEN               135             6.62                            893.70\n",
      "CTSDMK                95             8.67                            823.65\n",
      "CGKHND               105             7.57                            794.85\n",
      "JHBKTM               164             4.75                            779.00\n",
      "HNDPEN                98             7.57                            741.86\n",
      "CGKKIX                96             7.00                            672.00\n"
     ]
    }
   ],
   "source": [
    "# Group by 'route' and calculate the sum of 'booking_complete' and the mean of 'flight_duration'\n",
    "route_summary = df.groupby('route').agg({'booking_complete': 'sum', 'flight_duration': 'mean'})\n",
    "\n",
    "# Calculate the weighted booking completion rate for each route\n",
    "route_summary['weighted_booking_completion_rate'] = route_summary['booking_complete'] * route_summary['flight_duration']\n",
    "\n",
    "# Define the number of top routes to return\n",
    "top_n =   10\n",
    "\n",
    "# Sort the DataFrame by 'weighted_booking_completion_rate' in descending order and select the top N rows\n",
    "top_routes = route_summary.sort_values(by='weighted_booking_completion_rate', ascending=False).head(top_n)\n",
    "\n",
    "# Display the top N routes\n",
    "print(top_routes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_passengers</th>\n",
       "      <th>sales_channel</th>\n",
       "      <th>trip_type</th>\n",
       "      <th>flight_hour</th>\n",
       "      <th>flight_day</th>\n",
       "      <th>route</th>\n",
       "      <th>booking_origin</th>\n",
       "      <th>wants_extra_baggage</th>\n",
       "      <th>wants_preferred_seat</th>\n",
       "      <th>wants_in_flight_meals</th>\n",
       "      <th>flight_duration</th>\n",
       "      <th>booking_complete</th>\n",
       "      <th>purchase_lead_hours</th>\n",
       "      <th>length_of_stay_hours</th>\n",
       "      <th>flight_day_encoded</th>\n",
       "      <th>is_weekend_flight</th>\n",
       "      <th>time_of_day_preference</th>\n",
       "      <th>length_of_stay_hours_binned</th>\n",
       "      <th>purchase_lead_hours_binned</th>\n",
       "      <th>flight_duration_binned</th>\n",
       "      <th>wants_extra_baggage_and_preferred_seat</th>\n",
       "      <th>wants_meals_and_preferred_seat</th>\n",
       "      <th>wants_extra_baggage_and_meals</th>\n",
       "      <th>wants_all_services</th>\n",
       "      <th>wants_seat_and_meals_but_no_baggage</th>\n",
       "      <th>wants_baggage_and_meals_but_no_seat</th>\n",
       "      <th>wants_baggage_and_seat_but_no_meals</th>\n",
       "      <th>total_preferences</th>\n",
       "      <th>avg_purchase_lead_sales</th>\n",
       "      <th>std_purchase_lead_sales</th>\n",
       "      <th>avg_length_of_stay_sales</th>\n",
       "      <th>std_length_of_stay_sales</th>\n",
       "      <th>avg_purchase_lead_trip</th>\n",
       "      <th>std_purchase_lead_trip</th>\n",
       "      <th>avg_length_of_stay_trip</th>\n",
       "      <th>std_length_of_stay_trip</th>\n",
       "      <th>variance_flight_duration_by_route</th>\n",
       "      <th>variance_flight_duration_by_origin</th>\n",
       "      <th>mode_flight_day_by_route</th>\n",
       "      <th>mode_flight_day_by_origin</th>\n",
       "      <th>origin_latitude</th>\n",
       "      <th>origin_longitude</th>\n",
       "      <th>origin_city</th>\n",
       "      <th>origin_timezone</th>\n",
       "      <th>dest_latitude</th>\n",
       "      <th>dest_longitude</th>\n",
       "      <th>dest_city</th>\n",
       "      <th>dest_timezone</th>\n",
       "      <th>euclidean_distance</th>\n",
       "      <th>great_circle_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28546</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>6</td>\n",
       "      <td>Wed</td>\n",
       "      <td>KIXSUB</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1</td>\n",
       "      <td>168</td>\n",
       "      <td>72</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Short</td>\n",
       "      <td>Short</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "      <td>2036.749379</td>\n",
       "      <td>2171.215514</td>\n",
       "      <td>553.88666</td>\n",
       "      <td>814.683874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.324391</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>34.427299</td>\n",
       "      <td>135.244003</td>\n",
       "      <td>Osaka</td>\n",
       "      <td>Asia/Tokyo</td>\n",
       "      <td>-7.379830</td>\n",
       "      <td>112.787003</td>\n",
       "      <td>Surabaya</td>\n",
       "      <td>Asia/Jakarta</td>\n",
       "      <td>47.456854</td>\n",
       "      <td>5214.286601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1356</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>12</td>\n",
       "      <td>Wed</td>\n",
       "      <td>AKLKUL</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.83</td>\n",
       "      <td>1</td>\n",
       "      <td>5952</td>\n",
       "      <td>816</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>Long</td>\n",
       "      <td>Long</td>\n",
       "      <td>Long</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "      <td>2036.749379</td>\n",
       "      <td>2171.215514</td>\n",
       "      <td>553.88666</td>\n",
       "      <td>814.683874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.922464</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-37.008099</td>\n",
       "      <td>174.792007</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>Pacific/Auckland</td>\n",
       "      <td>2.745580</td>\n",
       "      <td>101.709999</td>\n",
       "      <td>Kuala Lumpur</td>\n",
       "      <td>Asia/Kuala_Lumpur</td>\n",
       "      <td>83.194561</td>\n",
       "      <td>8703.353493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22959</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>12</td>\n",
       "      <td>Mon</td>\n",
       "      <td>PENTPE</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.67</td>\n",
       "      <td>1</td>\n",
       "      <td>3000</td>\n",
       "      <td>1248</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>Long</td>\n",
       "      <td>Long</td>\n",
       "      <td>Short</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "      <td>2036.749379</td>\n",
       "      <td>2171.215514</td>\n",
       "      <td>553.88666</td>\n",
       "      <td>814.683874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.171768</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.297140</td>\n",
       "      <td>100.277000</td>\n",
       "      <td>Penang</td>\n",
       "      <td>Asia/Kuala_Lumpur</td>\n",
       "      <td>25.077700</td>\n",
       "      <td>121.233002</td>\n",
       "      <td>Taipei</td>\n",
       "      <td>Asia/Taipei</td>\n",
       "      <td>28.817087</td>\n",
       "      <td>3135.176429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21799</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>7</td>\n",
       "      <td>Sun</td>\n",
       "      <td>OOLSGN</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.83</td>\n",
       "      <td>1</td>\n",
       "      <td>600</td>\n",
       "      <td>1848</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Long</td>\n",
       "      <td>Short</td>\n",
       "      <td>Long</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "      <td>2036.749379</td>\n",
       "      <td>2171.215514</td>\n",
       "      <td>553.88666</td>\n",
       "      <td>814.683874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.748135</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-28.164400</td>\n",
       "      <td>153.505005</td>\n",
       "      <td>Gold Coast</td>\n",
       "      <td>Australia/Brisbane</td>\n",
       "      <td>10.818800</td>\n",
       "      <td>106.652000</td>\n",
       "      <td>Ho Chi Minh City</td>\n",
       "      <td>Asia/Ho_Chi_Minh</td>\n",
       "      <td>60.949930</td>\n",
       "      <td>6645.275618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27470</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>14</td>\n",
       "      <td>Sat</td>\n",
       "      <td>DPSPVG</td>\n",
       "      <td>China</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.33</td>\n",
       "      <td>1</td>\n",
       "      <td>1464</td>\n",
       "      <td>72</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>Short</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Short</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "      <td>2036.749379</td>\n",
       "      <td>2171.215514</td>\n",
       "      <td>553.88666</td>\n",
       "      <td>814.683874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.293775</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-8.748170</td>\n",
       "      <td>115.167000</td>\n",
       "      <td>Denpasar-Bali Island</td>\n",
       "      <td>Asia/Makassar</td>\n",
       "      <td>31.143400</td>\n",
       "      <td>121.805000</td>\n",
       "      <td>Shanghai</td>\n",
       "      <td>Asia/Shanghai</td>\n",
       "      <td>40.440084</td>\n",
       "      <td>4491.779172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22826</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>1</td>\n",
       "      <td>Sat</td>\n",
       "      <td>PENSYD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.58</td>\n",
       "      <td>1</td>\n",
       "      <td>840</td>\n",
       "      <td>504</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Evening</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "      <td>2036.749379</td>\n",
       "      <td>2171.215514</td>\n",
       "      <td>553.88666</td>\n",
       "      <td>814.683874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.748135</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.297140</td>\n",
       "      <td>100.277000</td>\n",
       "      <td>Penang</td>\n",
       "      <td>Asia/Kuala_Lumpur</td>\n",
       "      <td>-33.946098</td>\n",
       "      <td>151.177002</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>Australia/Sydney</td>\n",
       "      <td>64.271626</td>\n",
       "      <td>6894.616620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49967</th>\n",
       "      <td>3</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>13</td>\n",
       "      <td>Fri</td>\n",
       "      <td>PENTPE</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.67</td>\n",
       "      <td>1</td>\n",
       "      <td>6696</td>\n",
       "      <td>144</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Long</td>\n",
       "      <td>Short</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "      <td>2036.749379</td>\n",
       "      <td>2171.215514</td>\n",
       "      <td>553.88666</td>\n",
       "      <td>814.683874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.938638</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.297140</td>\n",
       "      <td>100.277000</td>\n",
       "      <td>Penang</td>\n",
       "      <td>Asia/Kuala_Lumpur</td>\n",
       "      <td>25.077700</td>\n",
       "      <td>121.233002</td>\n",
       "      <td>Taipei</td>\n",
       "      <td>Asia/Taipei</td>\n",
       "      <td>28.817087</td>\n",
       "      <td>3135.176429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25839</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>0</td>\n",
       "      <td>Thu</td>\n",
       "      <td>DMKHND</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.57</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Evening</td>\n",
       "      <td>Short</td>\n",
       "      <td>Short</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "      <td>2036.749379</td>\n",
       "      <td>2171.215514</td>\n",
       "      <td>553.88666</td>\n",
       "      <td>814.683874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.310104</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.912600</td>\n",
       "      <td>100.607002</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Asia/Bangkok</td>\n",
       "      <td>35.552299</td>\n",
       "      <td>139.779999</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>Asia/Tokyo</td>\n",
       "      <td>44.752657</td>\n",
       "      <td>4584.990470</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_passengers sales_channel  trip_type  flight_hour flight_day  \\\n",
       "28546               1      Internet  RoundTrip            6        Wed   \n",
       "1356                2      Internet  RoundTrip           12        Wed   \n",
       "22959               1      Internet  RoundTrip           12        Mon   \n",
       "21799               2      Internet  RoundTrip            7        Sun   \n",
       "27470               1      Internet  RoundTrip           14        Sat   \n",
       "22826               2      Internet  RoundTrip            1        Sat   \n",
       "49967               3      Internet  RoundTrip           13        Fri   \n",
       "25839               1      Internet  RoundTrip            0        Thu   \n",
       "\n",
       "        route booking_origin  wants_extra_baggage  wants_preferred_seat  \\\n",
       "28546  KIXSUB          Japan                    1                     0   \n",
       "1356   AKLKUL    Switzerland                    1                     0   \n",
       "22959  PENTPE         Taiwan                    1                     1   \n",
       "21799  OOLSGN      Australia                    1                     0   \n",
       "27470  DPSPVG          China                    0                     0   \n",
       "22826  PENSYD      Australia                    1                     0   \n",
       "49967  PENTPE       Malaysia                    1                     0   \n",
       "25839  DMKHND       Thailand                    0                     0   \n",
       "\n",
       "       wants_in_flight_meals  flight_duration  booking_complete  \\\n",
       "28546                      0             7.00                 1   \n",
       "1356                       1             8.83                 1   \n",
       "22959                      0             4.67                 1   \n",
       "21799                      1             8.83                 1   \n",
       "27470                      0             5.33                 1   \n",
       "22826                      1             8.58                 1   \n",
       "49967                      0             4.67                 1   \n",
       "25839                      0             7.57                 1   \n",
       "\n",
       "       purchase_lead_hours  length_of_stay_hours flight_day_encoded  \\\n",
       "28546                  168                    72                3.0   \n",
       "1356                  5952                   816                3.0   \n",
       "22959                 3000                  1248                1.0   \n",
       "21799                  600                  1848                7.0   \n",
       "27470                 1464                    72                6.0   \n",
       "22826                  840                   504                6.0   \n",
       "49967                 6696                   144                5.0   \n",
       "25839                   48                    48                4.0   \n",
       "\n",
       "       is_weekend_flight time_of_day_preference length_of_stay_hours_binned  \\\n",
       "28546                  0                Morning                       Short   \n",
       "1356                   0              Afternoon                        Long   \n",
       "22959                  0              Afternoon                        Long   \n",
       "21799                  1                Morning                        Long   \n",
       "27470                  1              Afternoon                       Short   \n",
       "22826                  1                Evening                      Medium   \n",
       "49967                  0              Afternoon                      Medium   \n",
       "25839                  0                Evening                       Short   \n",
       "\n",
       "      purchase_lead_hours_binned flight_duration_binned  \\\n",
       "28546                      Short                 Medium   \n",
       "1356                        Long                   Long   \n",
       "22959                       Long                  Short   \n",
       "21799                      Short                   Long   \n",
       "27470                     Medium                  Short   \n",
       "22826                     Medium                 Medium   \n",
       "49967                       Long                  Short   \n",
       "25839                      Short                 Medium   \n",
       "\n",
       "       wants_extra_baggage_and_preferred_seat  wants_meals_and_preferred_seat  \\\n",
       "28546                                       0                               0   \n",
       "1356                                        0                               0   \n",
       "22959                                       1                               0   \n",
       "21799                                       0                               0   \n",
       "27470                                       0                               0   \n",
       "22826                                       0                               0   \n",
       "49967                                       0                               0   \n",
       "25839                                       0                               0   \n",
       "\n",
       "       wants_extra_baggage_and_meals  wants_all_services  \\\n",
       "28546                              0                   0   \n",
       "1356                               1                   0   \n",
       "22959                              0                   0   \n",
       "21799                              1                   0   \n",
       "27470                              0                   0   \n",
       "22826                              1                   0   \n",
       "49967                              0                   0   \n",
       "25839                              0                   0   \n",
       "\n",
       "       wants_seat_and_meals_but_no_baggage  \\\n",
       "28546                                    0   \n",
       "1356                                     0   \n",
       "22959                                    0   \n",
       "21799                                    0   \n",
       "27470                                    0   \n",
       "22826                                    0   \n",
       "49967                                    0   \n",
       "25839                                    0   \n",
       "\n",
       "       wants_baggage_and_meals_but_no_seat  \\\n",
       "28546                                    0   \n",
       "1356                                     1   \n",
       "22959                                    0   \n",
       "21799                                    1   \n",
       "27470                                    0   \n",
       "22826                                    1   \n",
       "49967                                    0   \n",
       "25839                                    0   \n",
       "\n",
       "       wants_baggage_and_seat_but_no_meals  total_preferences  \\\n",
       "28546                                    0                  1   \n",
       "1356                                     0                  2   \n",
       "22959                                    1                  2   \n",
       "21799                                    0                  2   \n",
       "27470                                    0                  0   \n",
       "22826                                    0                  2   \n",
       "49967                                    0                  1   \n",
       "25839                                    0                  0   \n",
       "\n",
       "       avg_purchase_lead_sales  std_purchase_lead_sales  \\\n",
       "28546              2048.920373              2173.450504   \n",
       "1356               2048.920373              2173.450504   \n",
       "22959              2048.920373              2173.450504   \n",
       "21799              2048.920373              2173.450504   \n",
       "27470              2048.920373              2173.450504   \n",
       "22826              2048.920373              2173.450504   \n",
       "49967              2048.920373              2173.450504   \n",
       "25839              2048.920373              2173.450504   \n",
       "\n",
       "       avg_length_of_stay_sales  std_length_of_stay_sales  \\\n",
       "28546                570.532919                828.813233   \n",
       "1356                 570.532919                828.813233   \n",
       "22959                570.532919                828.813233   \n",
       "21799                570.532919                828.813233   \n",
       "27470                570.532919                828.813233   \n",
       "22826                570.532919                828.813233   \n",
       "49967                570.532919                828.813233   \n",
       "25839                570.532919                828.813233   \n",
       "\n",
       "       avg_purchase_lead_trip  std_purchase_lead_trip  \\\n",
       "28546             2036.749379             2171.215514   \n",
       "1356              2036.749379             2171.215514   \n",
       "22959             2036.749379             2171.215514   \n",
       "21799             2036.749379             2171.215514   \n",
       "27470             2036.749379             2171.215514   \n",
       "22826             2036.749379             2171.215514   \n",
       "49967             2036.749379             2171.215514   \n",
       "25839             2036.749379             2171.215514   \n",
       "\n",
       "       avg_length_of_stay_trip  std_length_of_stay_trip  \\\n",
       "28546                553.88666               814.683874   \n",
       "1356                 553.88666               814.683874   \n",
       "22959                553.88666               814.683874   \n",
       "21799                553.88666               814.683874   \n",
       "27470                553.88666               814.683874   \n",
       "22826                553.88666               814.683874   \n",
       "49967                553.88666               814.683874   \n",
       "25839                553.88666               814.683874   \n",
       "\n",
       "       variance_flight_duration_by_route  variance_flight_duration_by_origin  \\\n",
       "28546                                0.0                            0.324391   \n",
       "1356                                 0.0                            1.922464   \n",
       "22959                                0.0                            3.171768   \n",
       "21799                                0.0                            1.748135   \n",
       "27470                                0.0                            1.293775   \n",
       "22826                                0.0                            1.748135   \n",
       "49967                                0.0                            2.938638   \n",
       "25839                                0.0                            1.310104   \n",
       "\n",
       "       mode_flight_day_by_route  mode_flight_day_by_origin  origin_latitude  \\\n",
       "28546                       6.0                        7.0        34.427299   \n",
       "1356                        7.0                        4.0       -37.008099   \n",
       "22959                       2.0                        1.0         5.297140   \n",
       "21799                       4.0                        1.0       -28.164400   \n",
       "27470                       4.0                        1.0        -8.748170   \n",
       "22826                       3.0                        1.0         5.297140   \n",
       "49967                       2.0                        3.0         5.297140   \n",
       "25839                       4.0                        7.0        13.912600   \n",
       "\n",
       "       origin_longitude           origin_city     origin_timezone  \\\n",
       "28546        135.244003                 Osaka          Asia/Tokyo   \n",
       "1356         174.792007              Auckland    Pacific/Auckland   \n",
       "22959        100.277000                Penang   Asia/Kuala_Lumpur   \n",
       "21799        153.505005            Gold Coast  Australia/Brisbane   \n",
       "27470        115.167000  Denpasar-Bali Island       Asia/Makassar   \n",
       "22826        100.277000                Penang   Asia/Kuala_Lumpur   \n",
       "49967        100.277000                Penang   Asia/Kuala_Lumpur   \n",
       "25839        100.607002               Bangkok        Asia/Bangkok   \n",
       "\n",
       "       dest_latitude  dest_longitude         dest_city      dest_timezone  \\\n",
       "28546      -7.379830      112.787003          Surabaya       Asia/Jakarta   \n",
       "1356        2.745580      101.709999      Kuala Lumpur  Asia/Kuala_Lumpur   \n",
       "22959      25.077700      121.233002            Taipei        Asia/Taipei   \n",
       "21799      10.818800      106.652000  Ho Chi Minh City   Asia/Ho_Chi_Minh   \n",
       "27470      31.143400      121.805000          Shanghai      Asia/Shanghai   \n",
       "22826     -33.946098      151.177002            Sydney   Australia/Sydney   \n",
       "49967      25.077700      121.233002            Taipei        Asia/Taipei   \n",
       "25839      35.552299      139.779999             Tokyo         Asia/Tokyo   \n",
       "\n",
       "       euclidean_distance  great_circle_distance  \n",
       "28546           47.456854            5214.286601  \n",
       "1356            83.194561            8703.353493  \n",
       "22959           28.817087            3135.176429  \n",
       "21799           60.949930            6645.275618  \n",
       "27470           40.440084            4491.779172  \n",
       "22826           64.271626            6894.616620  \n",
       "49967           28.817087            3135.176429  \n",
       "25839           44.752657            4584.990470  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete_bookings_subset = df[df['booking_complete'] == True]\n",
    "\n",
    "complete_bookings_subset.sample(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RoundTrip     7453\n",
      "OneWay          20\n",
      "CircleTrip       5\n",
      "Name: trip_type, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "trip_type_counts = complete_bookings_subset['trip_type'].value_counts()\n",
    "\n",
    "# Now trip_type_counts contains the counts of unique elements in the \"trip_type\" column\n",
    "print(trip_type_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_passengers</th>\n",
       "      <th>sales_channel</th>\n",
       "      <th>trip_type</th>\n",
       "      <th>flight_hour</th>\n",
       "      <th>flight_day</th>\n",
       "      <th>route</th>\n",
       "      <th>booking_origin</th>\n",
       "      <th>wants_extra_baggage</th>\n",
       "      <th>wants_preferred_seat</th>\n",
       "      <th>wants_in_flight_meals</th>\n",
       "      <th>flight_duration</th>\n",
       "      <th>booking_complete</th>\n",
       "      <th>purchase_lead_hours</th>\n",
       "      <th>length_of_stay_hours</th>\n",
       "      <th>flight_day_encoded</th>\n",
       "      <th>is_weekend_flight</th>\n",
       "      <th>time_of_day_preference</th>\n",
       "      <th>length_of_stay_hours_binned</th>\n",
       "      <th>purchase_lead_hours_binned</th>\n",
       "      <th>flight_duration_binned</th>\n",
       "      <th>wants_extra_baggage_and_preferred_seat</th>\n",
       "      <th>wants_meals_and_preferred_seat</th>\n",
       "      <th>wants_extra_baggage_and_meals</th>\n",
       "      <th>wants_all_services</th>\n",
       "      <th>wants_seat_and_meals_but_no_baggage</th>\n",
       "      <th>wants_baggage_and_meals_but_no_seat</th>\n",
       "      <th>wants_baggage_and_seat_but_no_meals</th>\n",
       "      <th>total_preferences</th>\n",
       "      <th>avg_purchase_lead_sales</th>\n",
       "      <th>std_purchase_lead_sales</th>\n",
       "      <th>avg_length_of_stay_sales</th>\n",
       "      <th>std_length_of_stay_sales</th>\n",
       "      <th>avg_purchase_lead_trip</th>\n",
       "      <th>std_purchase_lead_trip</th>\n",
       "      <th>avg_length_of_stay_trip</th>\n",
       "      <th>std_length_of_stay_trip</th>\n",
       "      <th>variance_flight_duration_by_route</th>\n",
       "      <th>variance_flight_duration_by_origin</th>\n",
       "      <th>mode_flight_day_by_route</th>\n",
       "      <th>mode_flight_day_by_origin</th>\n",
       "      <th>origin_latitude</th>\n",
       "      <th>origin_longitude</th>\n",
       "      <th>origin_city</th>\n",
       "      <th>origin_timezone</th>\n",
       "      <th>dest_latitude</th>\n",
       "      <th>dest_longitude</th>\n",
       "      <th>dest_city</th>\n",
       "      <th>dest_timezone</th>\n",
       "      <th>euclidean_distance</th>\n",
       "      <th>great_circle_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43582</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>CircleTrip</td>\n",
       "      <td>11</td>\n",
       "      <td>Mon</td>\n",
       "      <td>CMBMEL</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.83</td>\n",
       "      <td>0</td>\n",
       "      <td>288</td>\n",
       "      <td>144</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Short</td>\n",
       "      <td>Long</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "      <td>2287.034483</td>\n",
       "      <td>2135.710426</td>\n",
       "      <td>605.37931</td>\n",
       "      <td>830.046386</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.748135</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.180760</td>\n",
       "      <td>79.884102</td>\n",
       "      <td>Colombo</td>\n",
       "      <td>Asia/Colombo</td>\n",
       "      <td>-37.673302</td>\n",
       "      <td>144.843002</td>\n",
       "      <td>Melbourne</td>\n",
       "      <td>Australia/Melbourne</td>\n",
       "      <td>78.940139</td>\n",
       "      <td>8358.219758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14241</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>0</td>\n",
       "      <td>Thu</td>\n",
       "      <td>ICNPER</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.62</td>\n",
       "      <td>0</td>\n",
       "      <td>1872</td>\n",
       "      <td>1968</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Evening</td>\n",
       "      <td>Long</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Short</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "      <td>2036.749379</td>\n",
       "      <td>2171.215514</td>\n",
       "      <td>553.88666</td>\n",
       "      <td>814.683874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.653680</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37.469101</td>\n",
       "      <td>126.450996</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>Asia/Seoul</td>\n",
       "      <td>-31.940300</td>\n",
       "      <td>115.967003</td>\n",
       "      <td>Perth</td>\n",
       "      <td>Australia/Perth</td>\n",
       "      <td>70.196717</td>\n",
       "      <td>7794.326313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22335</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>21</td>\n",
       "      <td>Thu</td>\n",
       "      <td>PEKSYD</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.58</td>\n",
       "      <td>0</td>\n",
       "      <td>2952</td>\n",
       "      <td>4392</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Evening</td>\n",
       "      <td>Long</td>\n",
       "      <td>Long</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "      <td>2036.749379</td>\n",
       "      <td>2171.215514</td>\n",
       "      <td>553.88666</td>\n",
       "      <td>814.683874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.583825</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>40.080101</td>\n",
       "      <td>116.584999</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>Asia/Shanghai</td>\n",
       "      <td>-33.946098</td>\n",
       "      <td>151.177002</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>Australia/Sydney</td>\n",
       "      <td>81.709760</td>\n",
       "      <td>8964.504325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31997</th>\n",
       "      <td>1</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>7</td>\n",
       "      <td>Sun</td>\n",
       "      <td>HKTPEK</td>\n",
       "      <td>China</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.42</td>\n",
       "      <td>0</td>\n",
       "      <td>504</td>\n",
       "      <td>96</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Short</td>\n",
       "      <td>Short</td>\n",
       "      <td>Short</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1956.815949</td>\n",
       "      <td>2148.483309</td>\n",
       "      <td>415.108580</td>\n",
       "      <td>662.501796</td>\n",
       "      <td>2036.749379</td>\n",
       "      <td>2171.215514</td>\n",
       "      <td>553.88666</td>\n",
       "      <td>814.683874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.293775</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.113200</td>\n",
       "      <td>98.316902</td>\n",
       "      <td>Phuket</td>\n",
       "      <td>Asia/Bangkok</td>\n",
       "      <td>40.080101</td>\n",
       "      <td>116.584999</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>Asia/Shanghai</td>\n",
       "      <td>36.818557</td>\n",
       "      <td>3990.428331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37326</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>6</td>\n",
       "      <td>Fri</td>\n",
       "      <td>DPSHND</td>\n",
       "      <td>Japan</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.57</td>\n",
       "      <td>0</td>\n",
       "      <td>2904</td>\n",
       "      <td>120</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Short</td>\n",
       "      <td>Long</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "      <td>2036.749379</td>\n",
       "      <td>2171.215514</td>\n",
       "      <td>553.88666</td>\n",
       "      <td>814.683874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.324391</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-8.748170</td>\n",
       "      <td>115.167000</td>\n",
       "      <td>Denpasar-Bali Island</td>\n",
       "      <td>Asia/Makassar</td>\n",
       "      <td>35.552299</td>\n",
       "      <td>139.779999</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>Asia/Tokyo</td>\n",
       "      <td>50.678707</td>\n",
       "      <td>5561.090496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33078</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>5</td>\n",
       "      <td>Fri</td>\n",
       "      <td>ICNVTZ</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.62</td>\n",
       "      <td>0</td>\n",
       "      <td>720</td>\n",
       "      <td>96</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Evening</td>\n",
       "      <td>Short</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Short</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "      <td>2036.749379</td>\n",
       "      <td>2171.215514</td>\n",
       "      <td>553.88666</td>\n",
       "      <td>814.683874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.653680</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37.469101</td>\n",
       "      <td>126.450996</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>Asia/Seoul</td>\n",
       "      <td>17.721201</td>\n",
       "      <td>83.224503</td>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>Asia/Kolkata</td>\n",
       "      <td>47.523776</td>\n",
       "      <td>4737.405742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_passengers sales_channel   trip_type  flight_hour flight_day  \\\n",
       "43582               1      Internet  CircleTrip           11        Mon   \n",
       "14241               2      Internet   RoundTrip            0        Thu   \n",
       "22335               1      Internet   RoundTrip           21        Thu   \n",
       "31997               1        Mobile   RoundTrip            7        Sun   \n",
       "37326               2      Internet   RoundTrip            6        Fri   \n",
       "33078               1      Internet   RoundTrip            5        Fri   \n",
       "\n",
       "        route booking_origin  wants_extra_baggage  wants_preferred_seat  \\\n",
       "43582  CMBMEL      Australia                    1                     1   \n",
       "14241  ICNPER    South Korea                    0                     0   \n",
       "22335  PEKSYD         Mexico                    1                     0   \n",
       "31997  HKTPEK          China                    0                     0   \n",
       "37326  DPSHND          Japan                    0                     1   \n",
       "33078  ICNVTZ    South Korea                    0                     0   \n",
       "\n",
       "       wants_in_flight_meals  flight_duration  booking_complete  \\\n",
       "43582                      1             8.83                 0   \n",
       "14241                      0             6.62                 0   \n",
       "22335                      0             8.58                 0   \n",
       "31997                      0             6.42                 0   \n",
       "37326                      1             7.57                 0   \n",
       "33078                      1             6.62                 0   \n",
       "\n",
       "       purchase_lead_hours  length_of_stay_hours flight_day_encoded  \\\n",
       "43582                  288                   144                1.0   \n",
       "14241                 1872                  1968                4.0   \n",
       "22335                 2952                  4392                4.0   \n",
       "31997                  504                    96                7.0   \n",
       "37326                 2904                   120                5.0   \n",
       "33078                  720                    96                5.0   \n",
       "\n",
       "       is_weekend_flight time_of_day_preference length_of_stay_hours_binned  \\\n",
       "43582                  0                Morning                      Medium   \n",
       "14241                  0                Evening                        Long   \n",
       "22335                  0                Evening                        Long   \n",
       "31997                  1                Morning                       Short   \n",
       "37326                  0                Morning                       Short   \n",
       "33078                  0                Evening                       Short   \n",
       "\n",
       "      purchase_lead_hours_binned flight_duration_binned  \\\n",
       "43582                      Short                   Long   \n",
       "14241                     Medium                  Short   \n",
       "22335                       Long                 Medium   \n",
       "31997                      Short                  Short   \n",
       "37326                       Long                 Medium   \n",
       "33078                     Medium                  Short   \n",
       "\n",
       "       wants_extra_baggage_and_preferred_seat  wants_meals_and_preferred_seat  \\\n",
       "43582                                       1                               1   \n",
       "14241                                       0                               0   \n",
       "22335                                       0                               0   \n",
       "31997                                       0                               0   \n",
       "37326                                       0                               1   \n",
       "33078                                       0                               0   \n",
       "\n",
       "       wants_extra_baggage_and_meals  wants_all_services  \\\n",
       "43582                              1                   1   \n",
       "14241                              0                   0   \n",
       "22335                              0                   0   \n",
       "31997                              0                   0   \n",
       "37326                              0                   0   \n",
       "33078                              0                   0   \n",
       "\n",
       "       wants_seat_and_meals_but_no_baggage  \\\n",
       "43582                                    0   \n",
       "14241                                    0   \n",
       "22335                                    0   \n",
       "31997                                    0   \n",
       "37326                                    1   \n",
       "33078                                    0   \n",
       "\n",
       "       wants_baggage_and_meals_but_no_seat  \\\n",
       "43582                                    0   \n",
       "14241                                    0   \n",
       "22335                                    0   \n",
       "31997                                    0   \n",
       "37326                                    0   \n",
       "33078                                    0   \n",
       "\n",
       "       wants_baggage_and_seat_but_no_meals  total_preferences  \\\n",
       "43582                                    0                  3   \n",
       "14241                                    0                  0   \n",
       "22335                                    0                  1   \n",
       "31997                                    0                  0   \n",
       "37326                                    0                  2   \n",
       "33078                                    0                  1   \n",
       "\n",
       "       avg_purchase_lead_sales  std_purchase_lead_sales  \\\n",
       "43582              2048.920373              2173.450504   \n",
       "14241              2048.920373              2173.450504   \n",
       "22335              2048.920373              2173.450504   \n",
       "31997              1956.815949              2148.483309   \n",
       "37326              2048.920373              2173.450504   \n",
       "33078              2048.920373              2173.450504   \n",
       "\n",
       "       avg_length_of_stay_sales  std_length_of_stay_sales  \\\n",
       "43582                570.532919                828.813233   \n",
       "14241                570.532919                828.813233   \n",
       "22335                570.532919                828.813233   \n",
       "31997                415.108580                662.501796   \n",
       "37326                570.532919                828.813233   \n",
       "33078                570.532919                828.813233   \n",
       "\n",
       "       avg_purchase_lead_trip  std_purchase_lead_trip  \\\n",
       "43582             2287.034483             2135.710426   \n",
       "14241             2036.749379             2171.215514   \n",
       "22335             2036.749379             2171.215514   \n",
       "31997             2036.749379             2171.215514   \n",
       "37326             2036.749379             2171.215514   \n",
       "33078             2036.749379             2171.215514   \n",
       "\n",
       "       avg_length_of_stay_trip  std_length_of_stay_trip  \\\n",
       "43582                605.37931               830.046386   \n",
       "14241                553.88666               814.683874   \n",
       "22335                553.88666               814.683874   \n",
       "31997                553.88666               814.683874   \n",
       "37326                553.88666               814.683874   \n",
       "33078                553.88666               814.683874   \n",
       "\n",
       "       variance_flight_duration_by_route  variance_flight_duration_by_origin  \\\n",
       "43582                                0.0                            1.748135   \n",
       "14241                                0.0                            0.653680   \n",
       "22335                                0.0                            2.583825   \n",
       "31997                                0.0                            1.293775   \n",
       "37326                                0.0                            0.324391   \n",
       "33078                                0.0                            0.653680   \n",
       "\n",
       "       mode_flight_day_by_route  mode_flight_day_by_origin  origin_latitude  \\\n",
       "43582                       3.0                        1.0         7.180760   \n",
       "14241                       1.0                        1.0        37.469101   \n",
       "22335                       1.0                        3.0        40.080101   \n",
       "31997                       4.0                        1.0         8.113200   \n",
       "37326                       7.0                        7.0        -8.748170   \n",
       "33078                       3.0                        1.0        37.469101   \n",
       "\n",
       "       origin_longitude           origin_city origin_timezone  dest_latitude  \\\n",
       "43582         79.884102               Colombo    Asia/Colombo     -37.673302   \n",
       "14241        126.450996                 Seoul      Asia/Seoul     -31.940300   \n",
       "22335        116.584999               Beijing   Asia/Shanghai     -33.946098   \n",
       "31997         98.316902                Phuket    Asia/Bangkok      40.080101   \n",
       "37326        115.167000  Denpasar-Bali Island   Asia/Makassar      35.552299   \n",
       "33078        126.450996                 Seoul      Asia/Seoul      17.721201   \n",
       "\n",
       "       dest_longitude      dest_city        dest_timezone  euclidean_distance  \\\n",
       "43582      144.843002      Melbourne  Australia/Melbourne           78.940139   \n",
       "14241      115.967003          Perth      Australia/Perth           70.196717   \n",
       "22335      151.177002         Sydney     Australia/Sydney           81.709760   \n",
       "31997      116.584999        Beijing        Asia/Shanghai           36.818557   \n",
       "37326      139.779999          Tokyo           Asia/Tokyo           50.678707   \n",
       "33078       83.224503  Visakhapatnam         Asia/Kolkata           47.523776   \n",
       "\n",
       "       great_circle_distance  \n",
       "43582            8358.219758  \n",
       "14241            7794.326313  \n",
       "22335            8964.504325  \n",
       "31997            3990.428331  \n",
       "37326            5561.090496  \n",
       "33078            4737.405742  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "incomplete_bookings_subset = df[df['booking_complete'] != True]\n",
    "incomplete_bookings_subset.sample(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RoundTrip     42044\n",
      "OneWay          367\n",
      "CircleTrip      111\n",
      "Name: trip_type, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "trip_type_counts = incomplete_bookings_subset['trip_type'].value_counts()\n",
    "\n",
    "# Now trip_type_counts contains the counts of unique elements in the \"trip_type\" column\n",
    "print(trip_type_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since hours of the day have a cyclical nature, you can encode flight_hour using trigonometric functions like sine and cosine. This transformation can capture the periodicity of time features and prevent issues such as treating 23:00 and 00:00 as far apart. The technique transforms the hour of the day into two continuous variables, which represent the cyclic pattern of time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform periodic encoding for flight_hour\n",
    "df['flight_hour_sin'] = np.sin(2 * np.pi * df['flight_hour'] / hours_in_day)\n",
    "df['flight_hour_cos'] = np.cos(2 * np.pi * df['flight_hour'] / hours_in_day)\n",
    "\n",
    "# Drop flight_hour\n",
    "df = df.drop(['flight_hour', 'flight_day'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalize the time-related columns to ensure they're on the same scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   num_passengers sales_channel  trip_type   route booking_origin  \\\n",
      "0               2      Internet  RoundTrip  AKLDEL    New Zealand   \n",
      "1               1      Internet  RoundTrip  AKLDEL    New Zealand   \n",
      "2               2      Internet  RoundTrip  AKLDEL          India   \n",
      "3               1      Internet  RoundTrip  AKLDEL    New Zealand   \n",
      "4               2      Internet  RoundTrip  AKLDEL          India   \n",
      "\n",
      "   wants_extra_baggage  wants_preferred_seat  wants_in_flight_meals  \\\n",
      "0                    1                     0                      0   \n",
      "1                    0                     0                      0   \n",
      "2                    1                     1                      0   \n",
      "3                    0                     0                      1   \n",
      "4                    1                     0                      1   \n",
      "\n",
      "   flight_duration  booking_complete  purchase_lead_hours  \\\n",
      "0         0.175983                 0             0.302191   \n",
      "1         0.175983                 0             0.129181   \n",
      "2         0.175983                 0             0.280277   \n",
      "3         0.175983                 0             0.110727   \n",
      "4         0.175983                 0             0.078431   \n",
      "\n",
      "   length_of_stay_hours flight_day_encoded  is_weekend_flight  \\\n",
      "0              0.024422                6.0                  1   \n",
      "1              0.025707                6.0                  1   \n",
      "2              0.028278                3.0                  0   \n",
      "3              0.039846                6.0                  1   \n",
      "4              0.028278                3.0                  0   \n",
      "\n",
      "  time_of_day_preference length_of_stay_hours_binned  \\\n",
      "0                Morning                      Medium   \n",
      "1                Evening                      Medium   \n",
      "2              Afternoon                      Medium   \n",
      "3                Evening                        Long   \n",
      "4              Afternoon                      Medium   \n",
      "\n",
      "  purchase_lead_hours_binned flight_duration_binned  \\\n",
      "0                       Long                  Short   \n",
      "1                       Long                  Short   \n",
      "2                       Long                  Short   \n",
      "3                       Long                  Short   \n",
      "4                     Medium                  Short   \n",
      "\n",
      "   wants_extra_baggage_and_preferred_seat  wants_meals_and_preferred_seat  \\\n",
      "0                                       0                               0   \n",
      "1                                       0                               0   \n",
      "2                                       1                               0   \n",
      "3                                       0                               0   \n",
      "4                                       0                               0   \n",
      "\n",
      "   wants_extra_baggage_and_meals  wants_all_services  \\\n",
      "0                              0                   0   \n",
      "1                              0                   0   \n",
      "2                              0                   0   \n",
      "3                              0                   0   \n",
      "4                              1                   0   \n",
      "\n",
      "   wants_seat_and_meals_but_no_baggage  wants_baggage_and_meals_but_no_seat  \\\n",
      "0                                    0                                    0   \n",
      "1                                    0                                    0   \n",
      "2                                    0                                    0   \n",
      "3                                    0                                    0   \n",
      "4                                    0                                    1   \n",
      "\n",
      "   wants_baggage_and_seat_but_no_meals  total_preferences  \\\n",
      "0                                    0                  1   \n",
      "1                                    0                  0   \n",
      "2                                    1                  2   \n",
      "3                                    0                  1   \n",
      "4                                    0                  2   \n",
      "\n",
      "   avg_purchase_lead_sales  std_purchase_lead_sales  avg_length_of_stay_sales  \\\n",
      "0              2048.920373              2173.450504                570.532919   \n",
      "1              2048.920373              2173.450504                570.532919   \n",
      "2              2048.920373              2173.450504                570.532919   \n",
      "3              2048.920373              2173.450504                570.532919   \n",
      "4              2048.920373              2173.450504                570.532919   \n",
      "\n",
      "   std_length_of_stay_sales  avg_purchase_lead_trip  std_purchase_lead_trip  \\\n",
      "0                828.813233             2036.749379             2171.215514   \n",
      "1                828.813233             2036.749379             2171.215514   \n",
      "2                828.813233             2036.749379             2171.215514   \n",
      "3                828.813233             2036.749379             2171.215514   \n",
      "4                828.813233             2036.749379             2171.215514   \n",
      "\n",
      "   avg_length_of_stay_trip  std_length_of_stay_trip  \\\n",
      "0                553.88666               814.683874   \n",
      "1                553.88666               814.683874   \n",
      "2                553.88666               814.683874   \n",
      "3                553.88666               814.683874   \n",
      "4                553.88666               814.683874   \n",
      "\n",
      "   variance_flight_duration_by_route  variance_flight_duration_by_origin  \\\n",
      "0                                0.0                            0.585116   \n",
      "1                                0.0                            0.585116   \n",
      "2                                0.0                            2.541387   \n",
      "3                                0.0                            0.585116   \n",
      "4                                0.0                            2.541387   \n",
      "\n",
      "   mode_flight_day_by_route  mode_flight_day_by_origin  origin_latitude  \\\n",
      "0                       6.0                        1.0       -37.008099   \n",
      "1                       6.0                        1.0       -37.008099   \n",
      "2                       6.0                        5.0       -37.008099   \n",
      "3                       6.0                        1.0       -37.008099   \n",
      "4                       6.0                        5.0       -37.008099   \n",
      "\n",
      "   origin_longitude origin_city   origin_timezone  dest_latitude  \\\n",
      "0        174.792007    Auckland  Pacific/Auckland        28.5665   \n",
      "1        174.792007    Auckland  Pacific/Auckland        28.5665   \n",
      "2        174.792007    Auckland  Pacific/Auckland        28.5665   \n",
      "3        174.792007    Auckland  Pacific/Auckland        28.5665   \n",
      "4        174.792007    Auckland  Pacific/Auckland        28.5665   \n",
      "\n",
      "   dest_longitude  dest_city dest_timezone  euclidean_distance  \\\n",
      "0       77.103104  New Delhi  Asia/Kolkata          117.656916   \n",
      "1       77.103104  New Delhi  Asia/Kolkata          117.656916   \n",
      "2       77.103104  New Delhi  Asia/Kolkata          117.656916   \n",
      "3       77.103104  New Delhi  Asia/Kolkata          117.656916   \n",
      "4       77.103104  New Delhi  Asia/Kolkata          117.656916   \n",
      "\n",
      "   great_circle_distance  flight_hour_sin  flight_hour_cos  \n",
      "0           12502.399655         0.965926        -0.258819  \n",
      "1           12502.399655         0.707107         0.707107  \n",
      "2           12502.399655        -0.965926        -0.258819  \n",
      "3           12502.399655         0.866025         0.500000  \n",
      "4           12502.399655        -0.707107        -0.707107  \n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Columns to normalize\n",
    "columns_to_normalize = ['purchase_lead_hours', 'length_of_stay_hours', 'flight_duration']\n",
    "\n",
    "# Initialize MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "# Fit and transform the selected columns\n",
    "df[columns_to_normalize] = scaler.fit_transform(df[columns_to_normalize])\n",
    "\n",
    "# Print first few rows to verify the changes\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['num_passengers', 'sales_channel', 'trip_type', 'route',\n",
       "       'booking_origin', 'wants_extra_baggage', 'wants_preferred_seat',\n",
       "       'wants_in_flight_meals', 'flight_duration', 'booking_complete',\n",
       "       'purchase_lead_hours', 'length_of_stay_hours', 'flight_day_encoded',\n",
       "       'is_weekend_flight', 'time_of_day_preference',\n",
       "       'length_of_stay_hours_binned', 'purchase_lead_hours_binned',\n",
       "       'flight_duration_binned', 'wants_extra_baggage_and_preferred_seat',\n",
       "       'wants_meals_and_preferred_seat', 'wants_extra_baggage_and_meals',\n",
       "       'wants_all_services', 'wants_seat_and_meals_but_no_baggage',\n",
       "       'wants_baggage_and_meals_but_no_seat',\n",
       "       'wants_baggage_and_seat_but_no_meals', 'total_preferences',\n",
       "       'avg_purchase_lead_sales', 'std_purchase_lead_sales',\n",
       "       'avg_length_of_stay_sales', 'std_length_of_stay_sales',\n",
       "       'avg_purchase_lead_trip', 'std_purchase_lead_trip',\n",
       "       'avg_length_of_stay_trip', 'std_length_of_stay_trip',\n",
       "       'variance_flight_duration_by_route',\n",
       "       'variance_flight_duration_by_origin', 'mode_flight_day_by_route',\n",
       "       'mode_flight_day_by_origin', 'origin_latitude', 'origin_longitude',\n",
       "       'origin_city', 'origin_timezone', 'dest_latitude', 'dest_longitude',\n",
       "       'dest_city', 'dest_timezone', 'euclidean_distance',\n",
       "       'great_circle_distance', 'flight_hour_sin', 'flight_hour_cos'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "# Instantiate the OneHotEncoder\n",
    "encoder = OneHotEncoder()\n",
    "\n",
    "# Fit the encoder to the categorical columns and transform the data\n",
    "one_hot_encoded = encoder.fit_transform(df[['sales_channel', 'trip_type', 'time_of_day_preference', 'length_of_stay_hours_binned', 'purchase_lead_hours_binned',\n",
    "       'flight_duration_binned']])\n",
    "\n",
    "# Convert the one-hot encoded result to a DataFrame\n",
    "one_hot_df = pd.DataFrame(one_hot_encoded.toarray(), columns=encoder.get_feature_names_out(['sales_channel', 'trip_type', 'time_of_day_preference', 'length_of_stay_hours_binned', 'purchase_lead_hours_binned',\n",
    "       'flight_duration_binned']))\n",
    "\n",
    "# Concatenate the one-hot encoded DataFrame with the original DataFrame\n",
    "df = pd.concat([df, one_hot_df], axis=1)\n",
    "\n",
    "# Drop the original categorical columns if needed\n",
    "df = df.drop(['sales_channel', 'trip_type', 'time_of_day_preference' , 'length_of_stay_hours_binned', 'purchase_lead_hours_binned', 'flight_duration_binned'], axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Label encoding route, origin_city, origin_timezone, dest_city, dest_timezone because they have several different categorical variable in them that would balloon the number of columns and reduce model interpretability if they are one hot encoded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "def label_encode_features(df, features):\n",
    "    \"\"\"\n",
    "    Encode categorical features in the DataFrame using LabelEncoder.\n",
    "    \n",
    "    Parameters:\n",
    "    - df: pandas DataFrame containing the data\n",
    "    - features: list of feature names to be encoded\n",
    "    \n",
    "    Returns:\n",
    "    - df: pandas DataFrame with encoded categorical features\n",
    "    \"\"\"\n",
    "    label_encoder = LabelEncoder()\n",
    "    for feature in features:\n",
    "        df[f'{feature}_encoded'] = label_encoder.fit_transform(df[feature])\n",
    "    return df\n",
    "\n",
    "# List of categorical features to be encoded\n",
    "categorical_features = ['route', 'booking_origin', 'origin_city', 'origin_timezone', 'dest_city', 'dest_timezone']\n",
    "\n",
    "# Encode categorical features\n",
    "df = label_encode_features(df, categorical_features)\n",
    "\n",
    "# Drop the original categorical columns if needed\n",
    "df = df.drop(['route', 'origin_city', 'origin_timezone', 'dest_city', 'dest_timezone', 'booking_origin'], axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "category_data = ['flight_day_encoded', 'mode_flight_day_by_route', 'mode_flight_day_by_origin']\n",
    "label_encoder = LabelEncoder()\n",
    "\n",
    "for col in category_data:\n",
    "    df[col] = label_encoder.fit_transform(df[col].astype(str)).astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chi-Square Test for Independence is used to determine whether there is a significant association between two categorical variables.\n",
    "We can use this test to assess the relationship between categorical features (e.g., 'sales_channel', 'trip_type') and the target variable ('booking_complete')."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Preprocessing for Clustering**\n",
    "\n",
    "- Started with PCA to perform the feature selection for KMeans Clustering\n",
    "- To determine no of components used cumulative explained variance\n",
    "- Categorical - OneHotEncoded\n",
    "- Numerical - StandardScaler\n",
    "- Missing vals - imputer(mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_passengers</th>\n",
       "      <th>wants_extra_baggage</th>\n",
       "      <th>wants_preferred_seat</th>\n",
       "      <th>wants_in_flight_meals</th>\n",
       "      <th>flight_duration</th>\n",
       "      <th>booking_complete</th>\n",
       "      <th>purchase_lead_hours</th>\n",
       "      <th>length_of_stay_hours</th>\n",
       "      <th>flight_day_encoded</th>\n",
       "      <th>is_weekend_flight</th>\n",
       "      <th>wants_extra_baggage_and_preferred_seat</th>\n",
       "      <th>wants_meals_and_preferred_seat</th>\n",
       "      <th>wants_extra_baggage_and_meals</th>\n",
       "      <th>wants_all_services</th>\n",
       "      <th>wants_seat_and_meals_but_no_baggage</th>\n",
       "      <th>wants_baggage_and_meals_but_no_seat</th>\n",
       "      <th>wants_baggage_and_seat_but_no_meals</th>\n",
       "      <th>total_preferences</th>\n",
       "      <th>avg_purchase_lead_sales</th>\n",
       "      <th>std_purchase_lead_sales</th>\n",
       "      <th>avg_length_of_stay_sales</th>\n",
       "      <th>std_length_of_stay_sales</th>\n",
       "      <th>avg_purchase_lead_trip</th>\n",
       "      <th>std_purchase_lead_trip</th>\n",
       "      <th>avg_length_of_stay_trip</th>\n",
       "      <th>std_length_of_stay_trip</th>\n",
       "      <th>variance_flight_duration_by_route</th>\n",
       "      <th>variance_flight_duration_by_origin</th>\n",
       "      <th>mode_flight_day_by_route</th>\n",
       "      <th>mode_flight_day_by_origin</th>\n",
       "      <th>origin_latitude</th>\n",
       "      <th>origin_longitude</th>\n",
       "      <th>dest_latitude</th>\n",
       "      <th>dest_longitude</th>\n",
       "      <th>euclidean_distance</th>\n",
       "      <th>great_circle_distance</th>\n",
       "      <th>flight_hour_sin</th>\n",
       "      <th>flight_hour_cos</th>\n",
       "      <th>sales_channel_Internet</th>\n",
       "      <th>sales_channel_Mobile</th>\n",
       "      <th>trip_type_CircleTrip</th>\n",
       "      <th>trip_type_OneWay</th>\n",
       "      <th>trip_type_RoundTrip</th>\n",
       "      <th>time_of_day_preference_Afternoon</th>\n",
       "      <th>time_of_day_preference_Evening</th>\n",
       "      <th>time_of_day_preference_Morning</th>\n",
       "      <th>length_of_stay_hours_binned_Long</th>\n",
       "      <th>length_of_stay_hours_binned_Medium</th>\n",
       "      <th>length_of_stay_hours_binned_Short</th>\n",
       "      <th>purchase_lead_hours_binned_Long</th>\n",
       "      <th>purchase_lead_hours_binned_Medium</th>\n",
       "      <th>purchase_lead_hours_binned_Short</th>\n",
       "      <th>flight_duration_binned_Long</th>\n",
       "      <th>flight_duration_binned_Medium</th>\n",
       "      <th>flight_duration_binned_Short</th>\n",
       "      <th>route_encoded</th>\n",
       "      <th>booking_origin_encoded</th>\n",
       "      <th>origin_city_encoded</th>\n",
       "      <th>origin_timezone_encoded</th>\n",
       "      <th>dest_city_encoded</th>\n",
       "      <th>dest_timezone_encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3750</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.175983</td>\n",
       "      <td>0</td>\n",
       "      <td>0.028835</td>\n",
       "      <td>0.052699</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2148.483309</td>\n",
       "      <td>415.108580</td>\n",
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       "      <td>2171.215514</td>\n",
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       "      <td>814.683874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.644216</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-6.125570</td>\n",
       "      <td>106.655998</td>\n",
       "      <td>28.566500</td>\n",
       "      <td>77.103104</td>\n",
       "      <td>45.573164</td>\n",
       "      <td>4990.178362</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-8.660254e-01</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>99</td>\n",
       "      <td>37</td>\n",
       "      <td>33</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>24707</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.033419</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>2171.215514</td>\n",
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       "      <td>151.177002</td>\n",
       "      <td>63.137722</td>\n",
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       "      <td>770</td>\n",
       "      <td>103</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>50</td>\n",
       "      <td>24</td>\n",
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       "    <tr>\n",
       "      <th>27543</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "      <td>2036.749379</td>\n",
       "      <td>2171.215514</td>\n",
       "      <td>553.88666</td>\n",
       "      <td>814.683874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.485051</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>22.308901</td>\n",
       "      <td>113.915001</td>\n",
       "      <td>-33.946098</td>\n",
       "      <td>151.177002</td>\n",
       "      <td>67.476527</td>\n",
       "      <td>7393.846164</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>366</td>\n",
       "      <td>34</td>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43785</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.153403</td>\n",
       "      <td>0.007712</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2171.215514</td>\n",
       "      <td>553.88666</td>\n",
       "      <td>814.683874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.938638</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.152000</td>\n",
       "      <td>76.401901</td>\n",
       "      <td>25.077700</td>\n",
       "      <td>121.233002</td>\n",
       "      <td>47.250440</td>\n",
       "      <td>5004.856403</td>\n",
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       "      <td>-7.071068e-01</td>\n",
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       "      <td>179</td>\n",
       "      <td>51</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>52</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>-37.008099</td>\n",
       "      <td>174.792007</td>\n",
       "      <td>2.745580</td>\n",
       "      <td>101.709999</td>\n",
       "      <td>83.194561</td>\n",
       "      <td>8703.353493</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>7.071068e-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30482</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.175983</td>\n",
       "      <td>0</td>\n",
       "      <td>0.467128</td>\n",
       "      <td>0.005141</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "      <td>2036.749379</td>\n",
       "      <td>2171.215514</td>\n",
       "      <td>553.88666</td>\n",
       "      <td>814.683874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.644216</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.566500</td>\n",
       "      <td>77.103104</td>\n",
       "      <td>3.558060</td>\n",
       "      <td>98.671700</td>\n",
       "      <td>33.024633</td>\n",
       "      <td>3596.307380</td>\n",
       "      <td>0.965926</td>\n",
       "      <td>2.588190e-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>263</td>\n",
       "      <td>37</td>\n",
       "      <td>55</td>\n",
       "      <td>8</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17844</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.016563</td>\n",
       "      <td>0</td>\n",
       "      <td>0.012687</td>\n",
       "      <td>0.080977</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "      <td>2036.749379</td>\n",
       "      <td>2171.215514</td>\n",
       "      <td>553.88666</td>\n",
       "      <td>814.683874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.938638</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.696600</td>\n",
       "      <td>85.359100</td>\n",
       "      <td>5.297140</td>\n",
       "      <td>100.277000</td>\n",
       "      <td>26.912442</td>\n",
       "      <td>2948.090072</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.123234e-17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>576</td>\n",
       "      <td>51</td>\n",
       "      <td>37</td>\n",
       "      <td>7</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13562</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.403727</td>\n",
       "      <td>0</td>\n",
       "      <td>0.054210</td>\n",
       "      <td>0.001285</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "      <td>2036.749379</td>\n",
       "      <td>2171.215514</td>\n",
       "      <td>553.88666</td>\n",
       "      <td>814.683874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.653680</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.469101</td>\n",
       "      <td>126.450996</td>\n",
       "      <td>1.641310</td>\n",
       "      <td>103.669998</td>\n",
       "      <td>42.457090</td>\n",
       "      <td>4615.206093</td>\n",
       "      <td>-0.707107</td>\n",
       "      <td>-7.071068e-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>416</td>\n",
       "      <td>85</td>\n",
       "      <td>67</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34080</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.362319</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003460</td>\n",
       "      <td>0.005141</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "      <td>2036.749379</td>\n",
       "      <td>2171.215514</td>\n",
       "      <td>553.88666</td>\n",
       "      <td>814.683874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.293775</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.080101</td>\n",
       "      <td>116.584999</td>\n",
       "      <td>5.297140</td>\n",
       "      <td>100.277000</td>\n",
       "      <td>38.416210</td>\n",
       "      <td>4197.857961</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>701</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46124</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0</td>\n",
       "      <td>0.021915</td>\n",
       "      <td>0.007712</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "      <td>2036.749379</td>\n",
       "      <td>2171.215514</td>\n",
       "      <td>553.88666</td>\n",
       "      <td>814.683874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.748135</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.308901</td>\n",
       "      <td>113.915001</td>\n",
       "      <td>-33.946098</td>\n",
       "      <td>151.177002</td>\n",
       "      <td>67.476527</td>\n",
       "      <td>7393.846164</td>\n",
       "      <td>-0.258819</td>\n",
       "      <td>-9.659258e-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>366</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_passengers  wants_extra_baggage  wants_preferred_seat  \\\n",
       "3750                1                    0                     0   \n",
       "24707               1                    1                     0   \n",
       "27543               1                    1                     1   \n",
       "43785               1                    0                     0   \n",
       "1258                1                    1                     1   \n",
       "30482               2                    0                     0   \n",
       "17844               1                    1                     0   \n",
       "13562               1                    1                     0   \n",
       "34080               1                    1                     1   \n",
       "46124               1                    1                     0   \n",
       "\n",
       "       wants_in_flight_meals  flight_duration  booking_complete  \\\n",
       "3750                       0         0.175983                 0   \n",
       "24707                      0         0.809524                 0   \n",
       "27543                      1         0.809524                 1   \n",
       "43785                      1         0.000000                 0   \n",
       "1258                       1         0.861284                 0   \n",
       "30482                      0         0.175983                 0   \n",
       "17844                      1         0.016563                 0   \n",
       "13562                      0         0.403727                 0   \n",
       "34080                      1         0.362319                 1   \n",
       "46124                      0         0.809524                 0   \n",
       "\n",
       "       purchase_lead_hours  length_of_stay_hours  flight_day_encoded  \\\n",
       "3750              0.028835              0.052699                 3.0   \n",
       "24707             0.023068              0.033419                 5.0   \n",
       "27543             0.059977              0.003856                 1.0   \n",
       "43785             0.153403              0.007712                 6.0   \n",
       "1258              0.221453              0.071979                 4.0   \n",
       "30482             0.467128              0.005141                 3.0   \n",
       "17844             0.012687              0.080977                 4.0   \n",
       "13562             0.054210              0.001285                 0.0   \n",
       "34080             0.003460              0.005141                 3.0   \n",
       "46124             0.021915              0.007712                 4.0   \n",
       "\n",
       "       is_weekend_flight  wants_extra_baggage_and_preferred_seat  \\\n",
       "3750                   0                                       0   \n",
       "24707                  1                                       0   \n",
       "27543                  0                                       1   \n",
       "43785                  1                                       0   \n",
       "1258                   0                                       1   \n",
       "30482                  0                                       0   \n",
       "17844                  0                                       0   \n",
       "13562                  0                                       0   \n",
       "34080                  0                                       1   \n",
       "46124                  0                                       0   \n",
       "\n",
       "       wants_meals_and_preferred_seat  wants_extra_baggage_and_meals  \\\n",
       "3750                                0                              0   \n",
       "24707                               0                              0   \n",
       "27543                               1                              1   \n",
       "43785                               0                              0   \n",
       "1258                                1                              1   \n",
       "30482                               0                              0   \n",
       "17844                               0                              1   \n",
       "13562                               0                              0   \n",
       "34080                               1                              1   \n",
       "46124                               0                              0   \n",
       "\n",
       "       wants_all_services  wants_seat_and_meals_but_no_baggage  \\\n",
       "3750                    0                                    0   \n",
       "24707                   0                                    0   \n",
       "27543                   1                                    0   \n",
       "43785                   0                                    0   \n",
       "1258                    1                                    0   \n",
       "30482                   0                                    0   \n",
       "17844                   0                                    0   \n",
       "13562                   0                                    0   \n",
       "34080                   1                                    0   \n",
       "46124                   0                                    0   \n",
       "\n",
       "       wants_baggage_and_meals_but_no_seat  \\\n",
       "3750                                     0   \n",
       "24707                                    0   \n",
       "27543                                    0   \n",
       "43785                                    0   \n",
       "1258                                     0   \n",
       "30482                                    0   \n",
       "17844                                    1   \n",
       "13562                                    0   \n",
       "34080                                    0   \n",
       "46124                                    0   \n",
       "\n",
       "       wants_baggage_and_seat_but_no_meals  total_preferences  \\\n",
       "3750                                     0                  0   \n",
       "24707                                    0                  1   \n",
       "27543                                    0                  3   \n",
       "43785                                    0                  1   \n",
       "1258                                     0                  3   \n",
       "30482                                    0                  0   \n",
       "17844                                    0                  2   \n",
       "13562                                    0                  1   \n",
       "34080                                    0                  3   \n",
       "46124                                    0                  1   \n",
       "\n",
       "       avg_purchase_lead_sales  std_purchase_lead_sales  \\\n",
       "3750               1956.815949              2148.483309   \n",
       "24707              2048.920373              2173.450504   \n",
       "27543              2048.920373              2173.450504   \n",
       "43785              1956.815949              2148.483309   \n",
       "1258               2048.920373              2173.450504   \n",
       "30482              2048.920373              2173.450504   \n",
       "17844              2048.920373              2173.450504   \n",
       "13562              2048.920373              2173.450504   \n",
       "34080              2048.920373              2173.450504   \n",
       "46124              2048.920373              2173.450504   \n",
       "\n",
       "       avg_length_of_stay_sales  std_length_of_stay_sales  \\\n",
       "3750                 415.108580                662.501796   \n",
       "24707                570.532919                828.813233   \n",
       "27543                570.532919                828.813233   \n",
       "43785                415.108580                662.501796   \n",
       "1258                 570.532919                828.813233   \n",
       "30482                570.532919                828.813233   \n",
       "17844                570.532919                828.813233   \n",
       "13562                570.532919                828.813233   \n",
       "34080                570.532919                828.813233   \n",
       "46124                570.532919                828.813233   \n",
       "\n",
       "       avg_purchase_lead_trip  std_purchase_lead_trip  \\\n",
       "3750              2036.749379             2171.215514   \n",
       "24707             2036.749379             2171.215514   \n",
       "27543             2036.749379             2171.215514   \n",
       "43785             2036.749379             2171.215514   \n",
       "1258              2036.749379             2171.215514   \n",
       "30482             2036.749379             2171.215514   \n",
       "17844             2036.749379             2171.215514   \n",
       "13562             2036.749379             2171.215514   \n",
       "34080             2036.749379             2171.215514   \n",
       "46124             2036.749379             2171.215514   \n",
       "\n",
       "       avg_length_of_stay_trip  std_length_of_stay_trip  \\\n",
       "3750                 553.88666               814.683874   \n",
       "24707                553.88666               814.683874   \n",
       "27543                553.88666               814.683874   \n",
       "43785                553.88666               814.683874   \n",
       "1258                 553.88666               814.683874   \n",
       "30482                553.88666               814.683874   \n",
       "17844                553.88666               814.683874   \n",
       "13562                553.88666               814.683874   \n",
       "34080                553.88666               814.683874   \n",
       "46124                553.88666               814.683874   \n",
       "\n",
       "       variance_flight_duration_by_route  variance_flight_duration_by_origin  \\\n",
       "3750                                 0.0                            1.644216   \n",
       "24707                                0.0                            1.700400   \n",
       "27543                                0.0                            2.485051   \n",
       "43785                                0.0                            2.938638   \n",
       "1258                                 0.0                            2.938638   \n",
       "30482                                0.0                            1.644216   \n",
       "17844                                0.0                            2.938638   \n",
       "13562                                0.0                            0.653680   \n",
       "34080                                0.0                            1.293775   \n",
       "46124                                0.0                            1.748135   \n",
       "\n",
       "       mode_flight_day_by_route  mode_flight_day_by_origin  origin_latitude  \\\n",
       "3750                        2.0                        0.0        -6.125570   \n",
       "24707                       0.0                        0.0        10.818800   \n",
       "27543                       0.0                        2.0        22.308901   \n",
       "43785                       5.0                        2.0        10.152000   \n",
       "1258                        6.0                        2.0       -37.008099   \n",
       "30482                       4.0                        0.0        28.566500   \n",
       "17844                       2.0                        2.0        27.696600   \n",
       "13562                       0.0                        0.0        37.469101   \n",
       "34080                       1.0                        0.0        40.080101   \n",
       "46124                       0.0                        0.0        22.308901   \n",
       "\n",
       "       origin_longitude  dest_latitude  dest_longitude  euclidean_distance  \\\n",
       "3750         106.655998      28.566500       77.103104           45.573164   \n",
       "24707        106.652000     -33.946098      151.177002           63.137722   \n",
       "27543        113.915001     -33.946098      151.177002           67.476527   \n",
       "43785         76.401901      25.077700      121.233002           47.250440   \n",
       "1258         174.792007       2.745580      101.709999           83.194561   \n",
       "30482         77.103104       3.558060       98.671700           33.024633   \n",
       "17844         85.359100       5.297140      100.277000           26.912442   \n",
       "13562        126.450996       1.641310      103.669998           42.457090   \n",
       "34080        116.584999       5.297140      100.277000           38.416210   \n",
       "46124        113.915001     -33.946098      151.177002           67.476527   \n",
       "\n",
       "       great_circle_distance  flight_hour_sin  flight_hour_cos  \\\n",
       "3750             4990.178362        -0.500000    -8.660254e-01   \n",
       "24707            6846.130404         0.965926    -2.588190e-01   \n",
       "27543            7393.846164         0.866025    -5.000000e-01   \n",
       "43785            5004.856403        -0.707107    -7.071068e-01   \n",
       "1258             8703.353493         0.707107     7.071068e-01   \n",
       "30482            3596.307380         0.965926     2.588190e-01   \n",
       "17844            2948.090072         1.000000     6.123234e-17   \n",
       "13562            4615.206093        -0.707107    -7.071068e-01   \n",
       "34080            4197.857961         0.866025    -5.000000e-01   \n",
       "46124            7393.846164        -0.258819    -9.659258e-01   \n",
       "\n",
       "       sales_channel_Internet  sales_channel_Mobile  trip_type_CircleTrip  \\\n",
       "3750                      0.0                   1.0                   0.0   \n",
       "24707                     1.0                   0.0                   0.0   \n",
       "27543                     1.0                   0.0                   0.0   \n",
       "43785                     0.0                   1.0                   0.0   \n",
       "1258                      1.0                   0.0                   0.0   \n",
       "30482                     1.0                   0.0                   0.0   \n",
       "17844                     1.0                   0.0                   0.0   \n",
       "13562                     1.0                   0.0                   0.0   \n",
       "34080                     1.0                   0.0                   0.0   \n",
       "46124                     1.0                   0.0                   0.0   \n",
       "\n",
       "       trip_type_OneWay  trip_type_RoundTrip  \\\n",
       "3750                0.0                  1.0   \n",
       "24707               0.0                  1.0   \n",
       "27543               0.0                  1.0   \n",
       "43785               0.0                  1.0   \n",
       "1258                0.0                  1.0   \n",
       "30482               0.0                  1.0   \n",
       "17844               0.0                  1.0   \n",
       "13562               0.0                  1.0   \n",
       "34080               0.0                  1.0   \n",
       "46124               0.0                  1.0   \n",
       "\n",
       "       time_of_day_preference_Afternoon  time_of_day_preference_Evening  \\\n",
       "3750                                1.0                             0.0   \n",
       "24707                               0.0                             0.0   \n",
       "27543                               0.0                             0.0   \n",
       "43785                               1.0                             0.0   \n",
       "1258                                0.0                             1.0   \n",
       "30482                               0.0                             1.0   \n",
       "17844                               0.0                             0.0   \n",
       "13562                               1.0                             0.0   \n",
       "34080                               0.0                             0.0   \n",
       "46124                               1.0                             0.0   \n",
       "\n",
       "       time_of_day_preference_Morning  length_of_stay_hours_binned_Long  \\\n",
       "3750                              0.0                               1.0   \n",
       "24707                             1.0                               1.0   \n",
       "27543                             1.0                               0.0   \n",
       "43785                             0.0                               0.0   \n",
       "1258                              0.0                               1.0   \n",
       "30482                             0.0                               0.0   \n",
       "17844                             1.0                               1.0   \n",
       "13562                             0.0                               0.0   \n",
       "34080                             1.0                               0.0   \n",
       "46124                             0.0                               0.0   \n",
       "\n",
       "       length_of_stay_hours_binned_Medium  length_of_stay_hours_binned_Short  \\\n",
       "3750                                  0.0                                0.0   \n",
       "24707                                 0.0                                0.0   \n",
       "27543                                 0.0                                1.0   \n",
       "43785                                 1.0                                0.0   \n",
       "1258                                  0.0                                0.0   \n",
       "30482                                 0.0                                1.0   \n",
       "17844                                 0.0                                0.0   \n",
       "13562                                 0.0                                1.0   \n",
       "34080                                 0.0                                1.0   \n",
       "46124                                 1.0                                0.0   \n",
       "\n",
       "       purchase_lead_hours_binned_Long  purchase_lead_hours_binned_Medium  \\\n",
       "3750                               0.0                                0.0   \n",
       "24707                              0.0                                0.0   \n",
       "27543                              0.0                                1.0   \n",
       "43785                              1.0                                0.0   \n",
       "1258                               1.0                                0.0   \n",
       "30482                              1.0                                0.0   \n",
       "17844                              0.0                                0.0   \n",
       "13562                              0.0                                1.0   \n",
       "34080                              0.0                                0.0   \n",
       "46124                              0.0                                0.0   \n",
       "\n",
       "       purchase_lead_hours_binned_Short  flight_duration_binned_Long  \\\n",
       "3750                                1.0                          0.0   \n",
       "24707                               1.0                          0.0   \n",
       "27543                               0.0                          0.0   \n",
       "43785                               0.0                          0.0   \n",
       "1258                                0.0                          1.0   \n",
       "30482                               0.0                          0.0   \n",
       "17844                               1.0                          0.0   \n",
       "13562                               0.0                          0.0   \n",
       "34080                               1.0                          0.0   \n",
       "46124                               1.0                          0.0   \n",
       "\n",
       "       flight_duration_binned_Medium  flight_duration_binned_Short  \\\n",
       "3750                             0.0                           1.0   \n",
       "24707                            1.0                           0.0   \n",
       "27543                            1.0                           0.0   \n",
       "43785                            0.0                           1.0   \n",
       "1258                             0.0                           0.0   \n",
       "30482                            0.0                           1.0   \n",
       "17844                            0.0                           1.0   \n",
       "13562                            0.0                           1.0   \n",
       "34080                            0.0                           1.0   \n",
       "46124                            1.0                           0.0   \n",
       "\n",
       "       route_encoded  booking_origin_encoded  origin_city_encoded  \\\n",
       "3750              99                      37                   33   \n",
       "24707            770                     103                   30   \n",
       "27543            366                      34                   31   \n",
       "43785            179                      51                   18   \n",
       "1258               6                      51                    2   \n",
       "30482            263                      37                   55   \n",
       "17844            576                      51                   37   \n",
       "13562            416                      85                   67   \n",
       "34080            701                      17                    8   \n",
       "46124            366                       4                   31   \n",
       "\n",
       "       origin_timezone_encoded  dest_city_encoded  dest_timezone_encoded  \n",
       "3750                         6                 30                      5  \n",
       "24707                        4                 50                     24  \n",
       "27543                        5                 50                     24  \n",
       "43785                        8                 52                     16  \n",
       "1258                        31                 18                      6  \n",
       "30482                        8                 27                      3  \n",
       "17844                        7                 32                      6  \n",
       "13562                       17                 40                      6  \n",
       "34080                       18                 32                      6  \n",
       "46124                        5                 50                     24  "
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows with missing values: 90\n"
     ]
    }
   ],
   "source": [
    "# Count the number of missing values in each row\n",
    "missing_rows = df.isnull().sum(axis=1)\n",
    "\n",
    "# Count the number of rows with missing values\n",
    "num_rows_with_missing_values = (missing_rows > 0).sum()\n",
    "\n",
    "print(\"Number of rows with missing values:\", num_rows_with_missing_values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop rows with missing values\n",
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardize the features\n",
    "scaler = StandardScaler()\n",
    "scaled_data = scaler.fit_transform(df)\n",
    "\n",
    "# Initialize PCA with desired number of components\n",
    "pca = PCA(n_components=60)\n",
    "\n",
    "# Fit PCA to the scaled data\n",
    "pca.fit(scaled_data)\n",
    "\n",
    "# Transform the data onto the new principal components\n",
    "transformed_data = pca.transform(scaled_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Determining cumulative explained variance\n",
    "explained_variance_ratio = pca.explained_variance_ratio_\n",
    "cumulative_explained_variance = np.cumsum(explained_variance_ratio)\n",
    "\n",
    "# Plotting side by side\n",
    "plt.figure(figsize=(15, 5))\n",
    "\n",
    "# Plot 1: Cumulative Explained Variance without adjusted scale\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(range(1, len(cumulative_explained_variance) + 1), cumulative_explained_variance)\n",
    "plt.xlabel('Number of Components')\n",
    "plt.ylabel('Cumulative Explained Variance')\n",
    "plt.title('Cumulative Explained Variance')\n",
    "plt.grid(True)\n",
    "\n",
    "# Plot 2: Cumulative Explained Variance with adjusted scale\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(range(1, len(cumulative_explained_variance) + 1), cumulative_explained_variance)\n",
    "plt.xlabel('Number of Components')\n",
    "plt.ylabel('Cumulative Explained Variance')\n",
    "plt.title('Cumulative Explained Variance (Zoomed In)')\n",
    "plt.xlim(1, 200)  # Adjust the x-axis limits to focus on the region of interest\n",
    "plt.grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At about 25 features, 80% of the variance is already captured and the curve starts to flatten out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot cumulative explained variance\n",
    "plt.figure(figsize=(15, 5))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(range(1, len(explained_variance_ratio) + 1), explained_variance_ratio, marker='o', linestyle='-')\n",
    "plt.xlabel('Number of Components')\n",
    "plt.ylabel('Explained Variance Ratio')\n",
    "plt.title('Explained Variance Ratio')\n",
    "plt.xlim(0, 200)\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(range(1, len(cumulative_explained_variance) + 1), cumulative_explained_variance, marker='o', linestyle='-')\n",
    "plt.xlabel('Number of Components')\n",
    "plt.ylabel('Cumulative Explained Variance')\n",
    "plt.title('Cumulative Explained Variance')\n",
    "plt.xlim(0, 200)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of transformed data: (49910, 25)\n"
     ]
    }
   ],
   "source": [
    "# Perform PCA with 25 components\n",
    "pca = PCA(n_components=25)\n",
    "X_pca = pca.fit_transform(df)\n",
    "\n",
    "# Check the shape of the transformed data\n",
    "print(\"Shape of transformed data:\", X_pca.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering\n",
    "\n",
    "to determine number of optimal clusters - elbow method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\USER\\anaconda3\\envs\\learn-env\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\USER\\anaconda3\\envs\\learn-env\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\USER\\anaconda3\\envs\\learn-env\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\USER\\anaconda3\\envs\\learn-env\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\USER\\anaconda3\\envs\\learn-env\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\USER\\anaconda3\\envs\\learn-env\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\USER\\anaconda3\\envs\\learn-env\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\USER\\anaconda3\\envs\\learn-env\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\USER\\anaconda3\\envs\\learn-env\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\USER\\anaconda3\\envs\\learn-env\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\USER\\anaconda3\\envs\\learn-env\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\USER\\anaconda3\\envs\\learn-env\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\USER\\anaconda3\\envs\\learn-env\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\USER\\anaconda3\\envs\\learn-env\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\USER\\anaconda3\\envs\\learn-env\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# Define a range of cluster numbers to try\n",
    "k_range = range(1, 16)  # You can adjust this range as needed\n",
    "\n",
    "# Fit KMeans for each value of k and calculate inertia\n",
    "inertia = []\n",
    "for k in k_range:\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "    kmeans.fit(X_pca)\n",
    "    inertia.append(kmeans.inertia_)\n",
    "\n",
    "# Plot the inertia vs. number of clusters\n",
    "plt.plot(k_range, inertia, marker='o')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('Inertia')\n",
    "plt.title('Elbow Method for Optimal Number of Clusters')\n",
    "plt.xticks(k_range)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After k=5 adding more clusters does not significantly impact change therefore k = 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\USER\\anaconda3\\envs\\learn-env\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster  booking_complete\n",
      "0        0                   11055\n",
      "         1                    1089\n",
      "1        0                    8461\n",
      "         1                    2035\n",
      "2        0                    8625\n",
      "         1                     874\n",
      "3        0                    5360\n",
      "         1                    1836\n",
      "4        0                    8946\n",
      "         1                    1629\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Initialize KMeans with 5 clusters\n",
    "kmeans = KMeans(n_clusters=5, random_state=42)\n",
    "\n",
    "# Fit KMeans to the PCA-transformed data\n",
    "kmeans.fit(X_pca)\n",
    "\n",
    "# Get the cluster labels\n",
    "cluster_labels = kmeans.labels_\n",
    "\n",
    "# Add the cluster labels to your dataframe\n",
    "df['cluster'] = cluster_labels\n",
    "\n",
    "# Group the dataframe by 'cluster' and 'booking_complete', then count the occurrences\n",
    "cluster_booking_counts = df.groupby(['cluster', 'booking_complete']).size()\n",
    "\n",
    "# Print the counts for each cluster and booking status\n",
    "print(cluster_booking_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         count   sum  percentage_complete\n",
      "cluster                                  \n",
      "0        12144  1089             8.967391\n",
      "1        10496  2035            19.388338\n",
      "2         9499   874             9.200969\n",
      "3         7196  1836            25.514175\n",
      "4        10575  1629            15.404255\n"
     ]
    }
   ],
   "source": [
    "# Group the data by cluster and calculate the total number of bookings and completed bookings for each cluster\n",
    "cluster_summary = df.groupby('cluster')['booking_complete'].agg(['count', 'sum'])\n",
    "\n",
    "# Calculate the percentage of completed bookings for each cluster\n",
    "cluster_summary['percentage_complete'] = (cluster_summary['sum'] / cluster_summary['count']) * 100\n",
    "\n",
    "# Print the summary\n",
    "print(cluster_summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_passengers</th>\n",
       "      <th>wants_extra_baggage</th>\n",
       "      <th>wants_preferred_seat</th>\n",
       "      <th>wants_in_flight_meals</th>\n",
       "      <th>flight_duration</th>\n",
       "      <th>booking_complete</th>\n",
       "      <th>purchase_lead_hours</th>\n",
       "      <th>length_of_stay_hours</th>\n",
       "      <th>flight_day_encoded</th>\n",
       "      <th>is_weekend_flight</th>\n",
       "      <th>wants_extra_baggage_and_preferred_seat</th>\n",
       "      <th>wants_meals_and_preferred_seat</th>\n",
       "      <th>wants_extra_baggage_and_meals</th>\n",
       "      <th>wants_all_services</th>\n",
       "      <th>wants_seat_and_meals_but_no_baggage</th>\n",
       "      <th>wants_baggage_and_meals_but_no_seat</th>\n",
       "      <th>wants_baggage_and_seat_but_no_meals</th>\n",
       "      <th>total_preferences</th>\n",
       "      <th>avg_purchase_lead_sales</th>\n",
       "      <th>std_purchase_lead_sales</th>\n",
       "      <th>avg_length_of_stay_sales</th>\n",
       "      <th>std_length_of_stay_sales</th>\n",
       "      <th>avg_purchase_lead_trip</th>\n",
       "      <th>std_purchase_lead_trip</th>\n",
       "      <th>avg_length_of_stay_trip</th>\n",
       "      <th>std_length_of_stay_trip</th>\n",
       "      <th>variance_flight_duration_by_route</th>\n",
       "      <th>variance_flight_duration_by_origin</th>\n",
       "      <th>mode_flight_day_by_route</th>\n",
       "      <th>mode_flight_day_by_origin</th>\n",
       "      <th>origin_latitude</th>\n",
       "      <th>origin_longitude</th>\n",
       "      <th>dest_latitude</th>\n",
       "      <th>dest_longitude</th>\n",
       "      <th>euclidean_distance</th>\n",
       "      <th>great_circle_distance</th>\n",
       "      <th>flight_hour_sin</th>\n",
       "      <th>flight_hour_cos</th>\n",
       "      <th>sales_channel_Internet</th>\n",
       "      <th>sales_channel_Mobile</th>\n",
       "      <th>trip_type_CircleTrip</th>\n",
       "      <th>trip_type_OneWay</th>\n",
       "      <th>trip_type_RoundTrip</th>\n",
       "      <th>time_of_day_preference_Afternoon</th>\n",
       "      <th>time_of_day_preference_Evening</th>\n",
       "      <th>time_of_day_preference_Morning</th>\n",
       "      <th>length_of_stay_hours_binned_Long</th>\n",
       "      <th>length_of_stay_hours_binned_Medium</th>\n",
       "      <th>length_of_stay_hours_binned_Short</th>\n",
       "      <th>purchase_lead_hours_binned_Long</th>\n",
       "      <th>purchase_lead_hours_binned_Medium</th>\n",
       "      <th>purchase_lead_hours_binned_Short</th>\n",
       "      <th>flight_duration_binned_Long</th>\n",
       "      <th>flight_duration_binned_Medium</th>\n",
       "      <th>flight_duration_binned_Short</th>\n",
       "      <th>route_encoded</th>\n",
       "      <th>booking_origin_encoded</th>\n",
       "      <th>origin_city_encoded</th>\n",
       "      <th>origin_timezone_encoded</th>\n",
       "      <th>dest_city_encoded</th>\n",
       "      <th>dest_timezone_encoded</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.175983</td>\n",
       "      <td>0</td>\n",
       "      <td>0.302191</td>\n",
       "      <td>0.024422</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "      <td>2036.749379</td>\n",
       "      <td>2171.215514</td>\n",
       "      <td>553.88666</td>\n",
       "      <td>814.683874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.585116</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-37.008099</td>\n",
       "      <td>174.792007</td>\n",
       "      <td>28.5665</td>\n",
       "      <td>77.103104</td>\n",
       "      <td>117.656916</td>\n",
       "      <td>12502.399655</td>\n",
       "      <td>0.965926</td>\n",
       "      <td>-0.258819</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.175983</td>\n",
       "      <td>0</td>\n",
       "      <td>0.129181</td>\n",
       "      <td>0.025707</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "      <td>2036.749379</td>\n",
       "      <td>2171.215514</td>\n",
       "      <td>553.88666</td>\n",
       "      <td>814.683874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.585116</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-37.008099</td>\n",
       "      <td>174.792007</td>\n",
       "      <td>28.5665</td>\n",
       "      <td>77.103104</td>\n",
       "      <td>117.656916</td>\n",
       "      <td>12502.399655</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.175983</td>\n",
       "      <td>0</td>\n",
       "      <td>0.280277</td>\n",
       "      <td>0.028278</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "      <td>2036.749379</td>\n",
       "      <td>2171.215514</td>\n",
       "      <td>553.88666</td>\n",
       "      <td>814.683874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.541387</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-37.008099</td>\n",
       "      <td>174.792007</td>\n",
       "      <td>28.5665</td>\n",
       "      <td>77.103104</td>\n",
       "      <td>117.656916</td>\n",
       "      <td>12502.399655</td>\n",
       "      <td>-0.965926</td>\n",
       "      <td>-0.258819</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.175983</td>\n",
       "      <td>0</td>\n",
       "      <td>0.110727</td>\n",
       "      <td>0.039846</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "      <td>2036.749379</td>\n",
       "      <td>2171.215514</td>\n",
       "      <td>553.88666</td>\n",
       "      <td>814.683874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.585116</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-37.008099</td>\n",
       "      <td>174.792007</td>\n",
       "      <td>28.5665</td>\n",
       "      <td>77.103104</td>\n",
       "      <td>117.656916</td>\n",
       "      <td>12502.399655</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.175983</td>\n",
       "      <td>0</td>\n",
       "      <td>0.078431</td>\n",
       "      <td>0.028278</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2048.920373</td>\n",
       "      <td>2173.450504</td>\n",
       "      <td>570.532919</td>\n",
       "      <td>828.813233</td>\n",
       "      <td>2036.749379</td>\n",
       "      <td>2171.215514</td>\n",
       "      <td>553.88666</td>\n",
       "      <td>814.683874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.541387</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-37.008099</td>\n",
       "      <td>174.792007</td>\n",
       "      <td>28.5665</td>\n",
       "      <td>77.103104</td>\n",
       "      <td>117.656916</td>\n",
       "      <td>12502.399655</td>\n",
       "      <td>-0.707107</td>\n",
       "      <td>-0.707107</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_passengers  wants_extra_baggage  wants_preferred_seat  \\\n",
       "0               2                    1                     0   \n",
       "1               1                    0                     0   \n",
       "2               2                    1                     1   \n",
       "3               1                    0                     0   \n",
       "4               2                    1                     0   \n",
       "\n",
       "   wants_in_flight_meals  flight_duration  booking_complete  \\\n",
       "0                      0         0.175983                 0   \n",
       "1                      0         0.175983                 0   \n",
       "2                      0         0.175983                 0   \n",
       "3                      1         0.175983                 0   \n",
       "4                      1         0.175983                 0   \n",
       "\n",
       "   purchase_lead_hours  length_of_stay_hours  flight_day_encoded  \\\n",
       "0             0.302191              0.024422                 5.0   \n",
       "1             0.129181              0.025707                 5.0   \n",
       "2             0.280277              0.028278                 2.0   \n",
       "3             0.110727              0.039846                 5.0   \n",
       "4             0.078431              0.028278                 2.0   \n",
       "\n",
       "   is_weekend_flight  wants_extra_baggage_and_preferred_seat  \\\n",
       "0                  1                                       0   \n",
       "1                  1                                       0   \n",
       "2                  0                                       1   \n",
       "3                  1                                       0   \n",
       "4                  0                                       0   \n",
       "\n",
       "   wants_meals_and_preferred_seat  wants_extra_baggage_and_meals  \\\n",
       "0                               0                              0   \n",
       "1                               0                              0   \n",
       "2                               0                              0   \n",
       "3                               0                              0   \n",
       "4                               0                              1   \n",
       "\n",
       "   wants_all_services  wants_seat_and_meals_but_no_baggage  \\\n",
       "0                   0                                    0   \n",
       "1                   0                                    0   \n",
       "2                   0                                    0   \n",
       "3                   0                                    0   \n",
       "4                   0                                    0   \n",
       "\n",
       "   wants_baggage_and_meals_but_no_seat  wants_baggage_and_seat_but_no_meals  \\\n",
       "0                                    0                                    0   \n",
       "1                                    0                                    0   \n",
       "2                                    0                                    1   \n",
       "3                                    0                                    0   \n",
       "4                                    1                                    0   \n",
       "\n",
       "   total_preferences  avg_purchase_lead_sales  std_purchase_lead_sales  \\\n",
       "0                  1              2048.920373              2173.450504   \n",
       "1                  0              2048.920373              2173.450504   \n",
       "2                  2              2048.920373              2173.450504   \n",
       "3                  1              2048.920373              2173.450504   \n",
       "4                  2              2048.920373              2173.450504   \n",
       "\n",
       "   avg_length_of_stay_sales  std_length_of_stay_sales  avg_purchase_lead_trip  \\\n",
       "0                570.532919                828.813233             2036.749379   \n",
       "1                570.532919                828.813233             2036.749379   \n",
       "2                570.532919                828.813233             2036.749379   \n",
       "3                570.532919                828.813233             2036.749379   \n",
       "4                570.532919                828.813233             2036.749379   \n",
       "\n",
       "   std_purchase_lead_trip  avg_length_of_stay_trip  std_length_of_stay_trip  \\\n",
       "0             2171.215514                553.88666               814.683874   \n",
       "1             2171.215514                553.88666               814.683874   \n",
       "2             2171.215514                553.88666               814.683874   \n",
       "3             2171.215514                553.88666               814.683874   \n",
       "4             2171.215514                553.88666               814.683874   \n",
       "\n",
       "   variance_flight_duration_by_route  variance_flight_duration_by_origin  \\\n",
       "0                                0.0                            0.585116   \n",
       "1                                0.0                            0.585116   \n",
       "2                                0.0                            2.541387   \n",
       "3                                0.0                            0.585116   \n",
       "4                                0.0                            2.541387   \n",
       "\n",
       "   mode_flight_day_by_route  mode_flight_day_by_origin  origin_latitude  \\\n",
       "0                       5.0                        0.0       -37.008099   \n",
       "1                       5.0                        0.0       -37.008099   \n",
       "2                       5.0                        4.0       -37.008099   \n",
       "3                       5.0                        0.0       -37.008099   \n",
       "4                       5.0                        4.0       -37.008099   \n",
       "\n",
       "   origin_longitude  dest_latitude  dest_longitude  euclidean_distance  \\\n",
       "0        174.792007        28.5665       77.103104          117.656916   \n",
       "1        174.792007        28.5665       77.103104          117.656916   \n",
       "2        174.792007        28.5665       77.103104          117.656916   \n",
       "3        174.792007        28.5665       77.103104          117.656916   \n",
       "4        174.792007        28.5665       77.103104          117.656916   \n",
       "\n",
       "   great_circle_distance  flight_hour_sin  flight_hour_cos  \\\n",
       "0           12502.399655         0.965926        -0.258819   \n",
       "1           12502.399655         0.707107         0.707107   \n",
       "2           12502.399655        -0.965926        -0.258819   \n",
       "3           12502.399655         0.866025         0.500000   \n",
       "4           12502.399655        -0.707107        -0.707107   \n",
       "\n",
       "   sales_channel_Internet  sales_channel_Mobile  trip_type_CircleTrip  \\\n",
       "0                     1.0                   0.0                   0.0   \n",
       "1                     1.0                   0.0                   0.0   \n",
       "2                     1.0                   0.0                   0.0   \n",
       "3                     1.0                   0.0                   0.0   \n",
       "4                     1.0                   0.0                   0.0   \n",
       "\n",
       "   trip_type_OneWay  trip_type_RoundTrip  time_of_day_preference_Afternoon  \\\n",
       "0               0.0                  1.0                               0.0   \n",
       "1               0.0                  1.0                               0.0   \n",
       "2               0.0                  1.0                               1.0   \n",
       "3               0.0                  1.0                               0.0   \n",
       "4               0.0                  1.0                               1.0   \n",
       "\n",
       "   time_of_day_preference_Evening  time_of_day_preference_Morning  \\\n",
       "0                             0.0                             1.0   \n",
       "1                             1.0                             0.0   \n",
       "2                             0.0                             0.0   \n",
       "3                             1.0                             0.0   \n",
       "4                             0.0                             0.0   \n",
       "\n",
       "   length_of_stay_hours_binned_Long  length_of_stay_hours_binned_Medium  \\\n",
       "0                               0.0                                 1.0   \n",
       "1                               0.0                                 1.0   \n",
       "2                               0.0                                 1.0   \n",
       "3                               1.0                                 0.0   \n",
       "4                               0.0                                 1.0   \n",
       "\n",
       "   length_of_stay_hours_binned_Short  purchase_lead_hours_binned_Long  \\\n",
       "0                                0.0                              1.0   \n",
       "1                                0.0                              1.0   \n",
       "2                                0.0                              1.0   \n",
       "3                                0.0                              1.0   \n",
       "4                                0.0                              0.0   \n",
       "\n",
       "   purchase_lead_hours_binned_Medium  purchase_lead_hours_binned_Short  \\\n",
       "0                                0.0                               0.0   \n",
       "1                                0.0                               0.0   \n",
       "2                                0.0                               0.0   \n",
       "3                                0.0                               0.0   \n",
       "4                                1.0                               0.0   \n",
       "\n",
       "   flight_duration_binned_Long  flight_duration_binned_Medium  \\\n",
       "0                          0.0                            0.0   \n",
       "1                          0.0                            0.0   \n",
       "2                          0.0                            0.0   \n",
       "3                          0.0                            0.0   \n",
       "4                          0.0                            0.0   \n",
       "\n",
       "   flight_duration_binned_Short  route_encoded  booking_origin_encoded  \\\n",
       "0                           1.0              0                      61   \n",
       "1                           1.0              0                      61   \n",
       "2                           1.0              0                      36   \n",
       "3                           1.0              0                      61   \n",
       "4                           1.0              0                      36   \n",
       "\n",
       "   origin_city_encoded  origin_timezone_encoded  dest_city_encoded  \\\n",
       "0                    2                       31                 30   \n",
       "1                    2                       31                 30   \n",
       "2                    2                       31                 30   \n",
       "3                    2                       31                 30   \n",
       "4                    2                       31                 30   \n",
       "\n",
       "   dest_timezone_encoded  cluster  \n",
       "0                      5        2  \n",
       "1                      5        2  \n",
       "2                      5        2  \n",
       "3                      5        2  \n",
       "4                      5        2  "
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A vanilla Logistic Regression model attempting to classify complete and incomplete bookings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 'booking_complete' is the target variable\n",
    "X = df.drop('booking_complete', axis=1)  # Features\n",
    "y = df['booking_complete']  # Target variable\n",
    "\n",
    "# Split data into training and testing sets (80% train, 20% test)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If predictors are on different scales, it can be challenging to interpret the coefficients correctly. Scaling the features ensures that each feature contributes proportionally to the result and can help the optimization algorithm converge faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\USER\\anaconda3\\envs\\learn-env\\lib\\site-packages\\sklearn\\base.py:458: UserWarning: X has feature names, but LogisticRegression was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Train the logistic regression model\n",
    "logreg = LogisticRegression(max_iter=10000)  # You may adjust max_iter based on convergence\n",
    "logreg.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Predict on the testing set\n",
    "y_pred = logreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's possible that our model needs further tuning or that certain classes have very few samples, making them difficult to predict accurately for this model. Additionally, consider a zero_division parameter to control the behavior of precision calculation when there are no predicted samples for a class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34                     euclidean_distance\n",
      "26      variance_flight_duration_by_route\n",
      "30                        origin_latitude\n",
      "1                     wants_extra_baggage\n",
      "16    wants_baggage_and_seat_but_no_meals\n",
      "55                          route_encoded\n",
      "45         time_of_day_preference_Morning\n",
      "58                origin_timezone_encoded\n",
      "28               mode_flight_day_by_route\n",
      "54           flight_duration_binned_Short\n",
      "Name: Feature, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Get coefficients and corresponding feature names\n",
    "coefficients = logreg.coef_[0]\n",
    "feature_names = df.columns[:-1]  # Exclude the target variable\n",
    "\n",
    "# Create a DataFrame to store coefficients and feature names\n",
    "coef_df = pd.DataFrame({'Feature': feature_names, 'Coefficient': coefficients})\n",
    "\n",
    "# Sort by absolute coefficient values\n",
    "coef_df['Abs_Coefficient'] = coef_df['Coefficient'].abs()\n",
    "coef_df = coef_df.sort_values(by='Abs_Coefficient', ascending=False)\n",
    "\n",
    "# Set the number of top features you want to output\n",
    "top_k = 10  # For example, outputting the top 10 features\n",
    "\n",
    "# Output the top features\n",
    "top_features = coef_df.head(top_k)['Feature']\n",
    "print(top_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\USER\\anaconda3\\envs\\learn-env\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1471: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\USER\\anaconda3\\envs\\learn-env\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1471: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\USER\\anaconda3\\envs\\learn-env\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1471: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class  0 - Precision: 0.8505309557202966\n",
      "Class  0 - Recall: 1.0\n",
      "Class  0 - F1-score: 0.9192291035080121\n",
      "Class  1 - Precision: 0.0\n",
      "Class  1 - Recall: 0.0\n",
      "Class  1 - F1-score: 0.0\n",
      "Accuracy: 0.8505309557202966\n",
      "Confusion Matrix:\n",
      "[[8490    0]\n",
      " [1492    0]]\n",
      "Macro Precision: 0.4252654778601483\n",
      "Macro Recall: 0.5\n",
      "Macro F1-score: 0.45961455175400606\n",
      "Weighted Precision: 0.7234029066384811\n",
      "Weighted Recall: 0.8505309557202966\n",
      "Weighted F1-score: 0.7818328079325809\n"
     ]
    }
   ],
   "source": [
    "# Calculate precision, recall, and F1-score for each class\n",
    "precision_scores = precision_score(y_test, y_pred, average=None)\n",
    "recall_scores = recall_score(y_test, y_pred, average=None)\n",
    "f1_scores = f1_score(y_test, y_pred, average=None)\n",
    "\n",
    "# Extract scores for class  0 and class  1\n",
    "precision_class_0 = precision_scores[0]\n",
    "precision_class_1 = precision_scores[1]\n",
    "\n",
    "recall_class_0 = recall_scores[0]\n",
    "recall_class_1 = recall_scores[1]\n",
    "\n",
    "f1_class_0 = f1_scores[0]\n",
    "f1_class_1 = f1_scores[1]\n",
    "\n",
    "# Print precision, recall, and F1-score for each class\n",
    "print(\"Class  0 - Precision:\", precision_class_0)\n",
    "print(\"Class  0 - Recall:\", recall_class_0)\n",
    "print(\"Class  0 - F1-score:\", f1_class_0)\n",
    "\n",
    "print(\"Class  1 - Precision:\", precision_class_1)\n",
    "print(\"Class  1 - Recall:\", recall_class_1)\n",
    "print(\"Class  1 - F1-score:\", f1_class_1)\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "# Print accuracy\n",
    "print(\"Accuracy:\", accuracy)\n",
    "\n",
    "# Calculate confusion matrix\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Print confusion matrix\n",
    "print(\"Confusion Matrix:\")\n",
    "print(conf_matrix)\n",
    "\n",
    "# Calculate macro and weighted averages\n",
    "macro_precision = precision_score(y_test, y_pred, average='macro')\n",
    "macro_recall = recall_score(y_test, y_pred, average='macro')\n",
    "macro_f1 = f1_score(y_test, y_pred, average='macro')\n",
    "\n",
    "weighted_precision = precision_score(y_test, y_pred, average='weighted')\n",
    "weighted_recall = recall_score(y_test, y_pred, average='weighted')\n",
    "weighted_f1 = f1_score(y_test, y_pred, average='weighted')\n",
    "\n",
    "# Print macro and weighted averages\n",
    "print(\"Macro Precision:\", macro_precision)\n",
    "print(\"Macro Recall:\", macro_recall)\n",
    "print(\"Macro F1-score:\", macro_f1)\n",
    "\n",
    "print(\"Weighted Precision:\", weighted_precision)\n",
    "print(\"Weighted Recall:\", weighted_recall)\n",
    "print(\"Weighted F1-score:\", weighted_f1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy: The accuracy of the model is 0.6954. It correctly predicts the booking completion status for approximately 69.54% of the instances in the test dataset.\n",
    "\n",
    "\n",
    "- **Precision:** Precision measures the accuracy of the positive predictions. For class 0 (incomplete bookings), the precision is 0.93, indicating that out of all instances predicted as incomplete bookings, 93.71% were correctly classified. For class 1 (complete bookings), the precision is low at 0.29, meaning that only 29% of instances predicted as complete bookings were actually classified correctly, indicating more false positives..\n",
    "- **Recall:** Recall measures the ability of the model to find all the relevant cases within a dataset. Class 0 has a recall of 68.80%, while Class 1 has a higher recall of 73.73%.\n",
    "**Accuracy:**: Overall accuracy of the model, which is 69.54%. It measures the proportion of correct predictions out of the total predictions.\n",
    "- **F1-score:** The F1-score is the harmonic mean of precision and recall, providing a balance between the two metrics. For class 0, the F1-score is high at 0.92, indicating a good balance between precision and recall. Class 0 has an F1-score of 79.35%, and Class 1 has an F1-score of 41.98%.\n",
    "\n",
    "- **Confusion Matrix:** The confusion matrix provides a detailed breakdown of the model's predictions. It is a matrix showing the counts of true positive, false positive, true negative, and false negative predictions. In this case, Class 0 has 5841 true positives and 1100 true negatives, while Class 1 has 2649 false negatives and 392 false positives.\n",
    "\n",
    "- **Macro and Weighted Averages:** Macro Precision, Recall, and F1-score: The unweighted mean of precision, recall, and F1-score across all classes. Macro metrics give equal weight to each class. The weighted average of precision, recall, and F1-score across all classes, weighted by the number of examples of each class. It's useful for imbalanced datasets. \n",
    "\n",
    "**Improvements and Shortcomings:**\n",
    "\n",
    "- Class Imbalance Handling\n",
    "\n",
    "- Feature Selection, techniques such as feature importance analysis or recursive feature elimination could improve model performance.\n",
    "\n",
    "- Model Selection\n",
    "\n",
    "- Hyperparameter Tuning\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An improved Random Forest model. Random Forests are invariant to monotonic transformations of the features. This means that scaling, shifting, or transforming the features linearly will not affect the performance of the Random Forest model. We will however undersample the majority class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6953516329392907\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.69      0.79      8490\n",
      "           1       0.29      0.74      0.42      1492\n",
      "\n",
      "    accuracy                           0.70      9982\n",
      "   macro avg       0.62      0.71      0.61      9982\n",
      "weighted avg       0.84      0.70      0.74      9982\n",
      "\n",
      "Confusion Matrix:\n",
      "[[5841 2649]\n",
      " [ 392 1100]]\n"
     ]
    }
   ],
   "source": [
    "# Undersample the majority class\n",
    "undersampler = RandomUnderSampler()\n",
    "X_train_resampled, y_train_resampled = undersampler.fit_resample(X_train, y_train)\n",
    "\n",
    "# Fit a Random Forest classifier\n",
    "classifier = RandomForestClassifier()\n",
    "classifier.fit(X_train_resampled, y_train_resampled)\n",
    "\n",
    "# Predict on the testing data\n",
    "y_pred = classifier.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)\n",
    "\n",
    "# Print classification report and confusion matrix\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(\"Confusion Matrix:\")\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation**\n",
    "- **Accuracy:** The overall accuracy of the model is 70.66%, indicating the proportion of correct predictions out of the total predictions.\n",
    "\n",
    "- **Precision and Recall:**\n",
    "    For class 0 (incomplete bookings):\n",
    "    Precision: 94% of the instances predicted as Class 0 are actually Class 0.\n",
    "    Recall: The model correctly identifies 70% of the actual Class 0 instances\n",
    "    F1-score: The harmonic mean of precision and recall for Class 0 is 80%.\n",
    "    Support: There are 8490 instances of Class 0 in the dataset.\n",
    "    For class 1 (complete bookings):\n",
    "    Precision: Only 30% of the instances predicted as Class 1 are actually Class 1.\n",
    "    Recall: Recall: The model correctly identifies 74% of the actual Class 1 instances.\n",
    "    F1-score: The harmonic mean of precision and recall for Class 1 is 43%.\n",
    "    Support: There are 1492 instances of Class 1 in the dataset.\n",
    "The weighted average precision, recall, and F1-score across both classes are provided under \"weighted avg.\"\n",
    "\n",
    "- **Confusion Matrix:**\n",
    "    True negatives (TN): 5950\n",
    "    False negatives (FN): 389\n",
    "    False positives (FP): 2540\n",
    "    True positives (TP): 1103\n",
    "\n",
    "\n",
    "**compare the performance with the previous logistic regression -model:**\n",
    "\n",
    "- **Accuracy:** random forest model accuracy of 0.8179\n",
    "    logistic regression model accuracy of 0.8527. The logistic regression model performed slightly better in terms of overall accuracy.\n",
    "\n",
    "- **Precision, Recall, and F1-score:** logistic regression model had slightly higher precision, recall, and F1-score for class 1 (complete bookings) compared to the random forest model.\n",
    "\n",
    "- **Confusion Matrix:**  the logistic regression model had fewer false positives and false negatives for class 1 compared to the random forest model.\n",
    "\n",
    "**Shortcomings/Improvements:**\n",
    "Overall, while the model achieves a relatively high accuracy, further examination of class-specific metrics reveals imbalances and areas for potential improvement, particularly in predicting Class 1.\n",
    "- Class Imbalance Handling: Further experimentation with different resampling techniques or adjusting class weights may improve performance.\n",
    "- Feature Selection: Feature importance analysis could help identify the most relevant features for predicting booking completion and improve model performance.\n",
    "- Hyperparameter Tuning: Fine-tuning hyperparameters of the Random Forest classifier could improve its performance.\n",
    "- Additional Evaluation Metrics: Consider using additional evaluation metrics such as ROC-AUC or precision-recall curve to assess model performance, especially considering the class imbalance.\n",
    "- Model Selection: Random Forest may not be the most suitable algorithm for this task. Trying other algorithms such as Gradient Boosting or Ensemble methods could potentially yield better results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An improved Random Forest Model       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ensemble Accuracy: 0.8459226607894209\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.95      0.91      8490\n",
      "           1       0.47      0.25      0.32      1492\n",
      "\n",
      "    accuracy                           0.85      9982\n",
      "   macro avg       0.67      0.60      0.62      9982\n",
      "weighted avg       0.82      0.85      0.82      9982\n",
      "\n",
      "Confusion Matrix:\n",
      "[[8078  412]\n",
      " [1126  366]]\n"
     ]
    }
   ],
   "source": [
    "# Step 1: Train Random Forest and XGBoost models independently\n",
    "rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "xgb_model = XGBClassifier(random_state=42)\n",
    "\n",
    "rf_model.fit(X_train, y_train)\n",
    "xgb_model.fit(X_train, y_train)\n",
    "\n",
    "# Step 2: Generate predictions\n",
    "rf_predictions_val = rf_model.predict(X_train)\n",
    "xgb_predictions_val = xgb_model.predict(X_train)\n",
    "\n",
    "# Step 3: Combine predictions\n",
    "combined_predictions_val = (rf_predictions_val + xgb_predictions_val) / 2\n",
    "\n",
    "# Step 4: Meta-model training\n",
    "meta_model = LogisticRegression(random_state=42)\n",
    "meta_model.fit(combined_predictions_val.reshape(-1, 1), y_train)\n",
    "\n",
    "# Step 5: Final prediction\n",
    "rf_predictions_test = rf_model.predict(X_test)\n",
    "xgb_predictions_test = xgb_model.predict(X_test)\n",
    "combined_predictions_test = (rf_predictions_test + xgb_predictions_test) / 2\n",
    "\n",
    "final_predictions = meta_model.predict(combined_predictions_test.reshape(-1, 1))\n",
    "\n",
    "# Evaluate the performance of the ensemble model\n",
    "ensemble_accuracy = accuracy_score(y_test, final_predictions)\n",
    "print(\"Ensemble Accuracy:\", ensemble_accuracy)\n",
    "\n",
    "# Calculate classification report\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(y_test, final_predictions))\n",
    "\n",
    "# Calculate confusion matrix\n",
    "print(\"Confusion Matrix:\")\n",
    "print(confusion_matrix(y_test, final_predictions))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 Most Important Features:\n",
      "1. route_encoded: Importance = 0.07085786393270667\n",
      "2. booking_complete: Importance = 0.05807210809944188\n",
      "3. variance_flight_duration_by_route: Importance = 0.0563579249833672\n",
      "4. purchase_lead_hours: Importance = 0.04401612627352612\n",
      "5. flight_hour_sin: Importance = 0.03838049735649433\n",
      "6. great_circle_distance: Importance = 0.038158133363390405\n",
      "7. length_of_stay_hours: Importance = 0.03593816681694341\n",
      "8. wants_baggage_and_seat_but_no_meals: Importance = 0.03237085798947302\n",
      "9. euclidean_distance: Importance = 0.030039861162040524\n",
      "10. dest_longitude: Importance = 0.02820237321725627\n"
     ]
    }
   ],
   "source": [
    "# Calculate feature importances from Random Forest and XGBoost models\n",
    "rf_importances = rf_model.feature_importances_\n",
    "xgb_importances = xgb_model.feature_importances_\n",
    "\n",
    "# Combine feature importances from both models\n",
    "combined_importances = (rf_importances + xgb_importances) /  2\n",
    "\n",
    "# Get indices of top N most important features\n",
    "top_indices = np.argsort(combined_importances)[::-1][:]\n",
    "\n",
    "# Get the names of top N most important features\n",
    "# Use the column names from the DataFrame df\n",
    "your_feature_names = df.columns.tolist()\n",
    "top_features = [your_feature_names[i] for i in top_indices]\n",
    "\n",
    "# Define N as the number of top features you want to display\n",
    "N =  10  # Example value, replace with the desired number\n",
    "\n",
    "# Print the top N most important features\n",
    "print(f\"Top {N} Most Important Features:\")\n",
    "for i, feature in enumerate(top_features[:N],  1):\n",
    "    print(f\"{i}. {feature}: Importance = {combined_importances[top_indices[i-1]]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation:**\n",
    "\n",
    "Accuracy: The accuracy of the ensemble model is 84.64%, which indicates the proportion of correct predictions out of the total predictions.\n",
    "Precision and recall:\n",
    "\n",
    "Precision: 88% of the instances predicted as Class 0 are actually Class 0.\n",
    "\n",
    "Recall: The model correctly identifies 95% of the actual Class 0 instances.\n",
    "\n",
    "F1-score: The harmonic mean of precision and recall for Class 0 is 91%.\n",
    "\n",
    "Support: There are 8490 instances of Class 0 in the dataset.\n",
    "For Class 1:\n",
    "Only 47% of the instances predicted as Class 1 are True Class 1.\n",
    "\n",
    "Recall: The model correctly identifies 24% of the actual Class 1 instances.\n",
    "\n",
    "F1-score: The harmonic mean of precision and recall for Class 1 is 32%.\n",
    "\n",
    "Support: There are 1492 instances of Class 1 in the dataset.\n",
    "\n",
    "True negatives (TN): 8095\n",
    "False negatives (FN): 1138\n",
    "False positives (FP): 395\n",
    "True positives (TP): 354\n",
    "\n",
    "The ensemble model performs better at predicting Class 0, as indicated by higher precision, recall, and F1-score compared to Class 1.\n",
    "Class 1 has a lower precision and recall, suggesting that the model struggles more with this class, potentially due to class imbalance or other factors.\n",
    "The confusion matrix shows the distribution of correct and incorrect predictions across both classes, with a higher number of true negatives (correctly predicted Class 0) compared to true positives (correctly predicted Class 1).\n",
    "Overall, while the ensemble model achieves a relatively high accuracy, there is room for improvement in predicting Class 1, particularly in terms of precision and recall. Further analysis and potentially model refinement may be necessary to address these issues.\n",
    "\n",
    "\n",
    "**ROC-AUC and Precision-Recall curve**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate predicted probabilities for the positive class (class 1)\n",
    "y_probs = final_predictions  # Using predictions from the ensemble model\n",
    "\n",
    "# Calculate ROC curve\n",
    "fpr, tpr, _ = roc_curve(y_test, y_probs)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Calculate precision-recall curve\n",
    "precision, recall, _ = precision_recall_curve(y_test, y_probs)\n",
    "\n",
    "# Plot both curves side by side\n",
    "plt.figure(figsize=(14, 6))\n",
    "\n",
    "# Plot ROC curve\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(fpr, tpr, color='blue', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "\n",
    "# Plot precision-recall curve\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(recall, precision, color='green', lw=2, label='Precision-Recall curve')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.title('Precision-Recall Curve')\n",
    "plt.legend(loc=\"lower left\")\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Receiver Operating Characteristic (ROC) Curve:\n",
    "\n",
    "A curve that is closer to the top-left corner represents better model performance, with higher AUC indicating better discrimination between the classes. The area under the ROC curve (AUC-ROC) provides a single scalar value to summarize the overall performance of the model. An AUC-ROC value closer to 1 indicates better performance. Model's is 0.63, straight lines are due to a big jump in TPR or FPR for a small change in the classification threshold. This is happening because a large proportion of the negative cases have the same value. \n",
    "\n",
    "## Precision-Recall Curve:\n",
    "\n",
    "A curve that is closer to the top-right corner represents better model performance, with higher precision and recall. The precision-recall curve provides insights into the trade-off between precision and recall for different decision thresholds. Depending on the specific use case, you may prioritize precision over recall or vice versa.\n",
    "\n",
    "## SUMMARY\n",
    "\n",
    "- **Ensemble Accuracy:** 84.64%\n",
    "\n",
    "  The proportion of correct predictions out of the total predictions.\n",
    "\n",
    "- **Precision:**\n",
    "  - Class 0: 88%\n",
    "  - Class 1: 47%\n",
    "  \n",
    "  Precision measures the accuracy of the positive predictions.\n",
    "\n",
    "- **Recall:**\n",
    "  - Class 0: 95%\n",
    "  - Class 1: 24%\n",
    "  \n",
    "  Recall measures the ability of the model to find all relevant cases within a dataset.\n",
    "\n",
    "- **F1-score:**\n",
    "  - Class 0: 91%\n",
    "  - Class 1: 32%\n",
    "  \n",
    "  F1-score is the harmonic mean of precision and recall, providing a balance between the two metrics.\n",
    "\n",
    "- **Support:**\n",
    "  - Class 0: 8490 instances\n",
    "  - Class 1: 1492 instances\n",
    "  \n",
    "  Support represents the number of occurrences of each class in the dataset.\n",
    "\n",
    "- **Confusion Matrix:**\n",
    "  - True Negatives (TN): 8095\n",
    "  - False Negatives (FN): 1138\n",
    "  - False Positives (FP): 395\n",
    "  - True Positives (TP): 354\n",
    "  \n",
    "  The confusion matrix shows the distribution of correct and incorrect predictions across both classes.\n",
    "\n",
    "### Random Forest Model:\n",
    "\n",
    "- **Accuracy:** 84.64%\n",
    "\n",
    "- **Precision:**\n",
    "  - Class 0: 88%\n",
    "  - Class 1: 47%\n",
    "\n",
    "- **Recall:**\n",
    "  - Class 0: 95%\n",
    "  - Class 1: 24%\n",
    "\n",
    "- **F1-score:**\n",
    "  - Class 0: 91%\n",
    "  - Class 1: 32%\n",
    "\n",
    "- **Support:**\n",
    "  - Class 0: 8490 instances\n",
    "  - Class 1: 1492 instances\n",
    "\n",
    "- **Confusion Matrix:**\n",
    "  - True Negatives (TN): 8095\n",
    "  - False Negatives (FN): 1138\n",
    "  - False Positives (FP): 395\n",
    "  - True Positives (TP): 354\n",
    "\n",
    "### Logistic Regression Model:\n",
    "\n",
    "- **Accuracy:** 70.66%\n",
    "\n",
    "- **Precision:**\n",
    "  - Class 0: 94%\n",
    "  - Class 1: 30%\n",
    "\n",
    "- **Recall:**\n",
    "  - Class 0: 70%\n",
    "  - Class 1: 74%\n",
    "\n",
    "- **F1-score:**\n",
    "  - Class 0: 80%\n",
    "  - Class 1: 43%\n",
    "\n",
    "- **Support:**\n",
    "  - Class 0: 8490 instances\n",
    "  - Class 1: 1492 instances\n",
    "\n",
    "- **Confusion Matrix:**\n",
    "  - True Negatives (TN): 8095\n",
    "  - False Negatives (FN): 1138\n",
    "  - False Positives (FP): 395\n",
    "  - True Positives (TP): 354\n"
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    "## Evaluation\n",
    "\n",
    "The ensemble model doesn't show a significant improvement in performance compared to the Random Forest model. While the accuracy is comparable, there's no substantial enhancement in precision, recall, or F1-score."
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