Job-recommendation-system-engage

📖 JOBS RECOMMENDATION SYSTEM

🧐 Get the most suitable job for yourself!!

📋 Table of Contents

➡️ Overview

➡️ Tech Stack

➡️ How to run the project?

➡️ Similarity Score

➡️ How Cosine Similarity works?

➡️ Decision Tree Recommendation

➡️ Content Based Filtering

➡️ Flowchart

➡️ Dependencies

➡️ Data Source

👩‍ Overview

It is a web-based application that recommends the most suitable jobs for the user by using various algorithms like decision tree, cosine similarity,user's liking , "user with similar education joined what" etc. Apart from recommending the best jobs this app also helps to visualize various queries related to jobs with interactive plots like correlational heat maps , location wise availability of jobs, most jobs producing skills , experience required ,top sectors, vacancies available etc.

👬 Features

1.Recommendations on basis of Location , Industry and Experience

2.Recommendations of jobs of similar types based on cosine similarity.

3.Want to change your industry?

4.Correlational Heatmaps

5.Interactive graph visualizations

6.Location-wise jobs

7.Company and sectors with highest no. of jobs etc.

Link to youtube demo: https://www.youtube.com/watch?v=qTLlDMXcD6Y

👩‍ How to run the project?

1. Clone or download this repository to your local machine.

2. Install all the libraries mentioned in the Dependencies

3. Get the data from dataset folder in this drive link

4. Open your terminal/command prompt from your project directory and run the file in virtual environment.

5. Type the command [streamlit run app.py]

6. Hurray! That's it.

Similarity Score

How does it decide which item is most similar to the item user likes? Here come the similarity scores.

It is a numerical value ranges between zero to one which helps to determine how much two items are similar to each other on a scale of zero to one. This similarity score is obtained measuring the similarity between the text details of both of the items. So, similarity score is the measure of similarity between given text details of two items. This can be done by cosine-similarity.

How Cosine Similarity works?

Cosine similarity is a metric used to measure how similar the documents are irrespective of their size. Mathematically, it measures the cosine of the angle between two vectors projected in a multi-dimensional space. The cosine similarity is advantageous because even if the two similar documents are far apart by the Euclidean distance (due to the size of the document), chances are they may still be oriented closer together. The smaller the angle, higher the cosine similarity.

Decision Tree Recommendation

A decision tree is a type of supervised machine learning used to categorize or make predictions based on how a previous set of questions were answered. The model is a form of supervised learning, meaning that the model is trained and tested on a set of data that contains the desired categorization.

Content Based Filtering

Content-based filtering is a type of recommender system that attempts to guess what a user may like based on that user's activity.

Flowchart

Dependencies :

1. matplot.lib

2. numpy

3. pandas

4. requests

5. seaborn

6. streamlit

7. streamlit_option_menu

8. wordcloud

Data source

Dataset is downloaded from naukri.com

Get the entire data from this link.