Premier Experience for Loyal eCommerce Customers

dell_hackathon_iiitb_2018_aksit

About

• This project is a simple illustration of a Recommendation System to enhance the Premier Experience of Loyal eCommerce Customers
• For simplicity we have worked only on books rating dataset
• Soul intention of this project was to develop Recommendation System and not the Front End

Layout

• Identify the user as guest or existing user
• If new user, promote for signup
• For new users recommendations are based on popularity index i.e. He/She will be recommended most popular items
• For existing users recommendations are based on Item-Based & User-Based Collabrative Filtering
• Item-Based Collabrative Filtering is recommending items baased on Item Similarity Index i.e. items which user has purchased or liked in past.
• User-Based Collabrative Filtering is recommending items based on User Similarity Index i.e. items which other customers similar to user likes.

Tools & Libraries Required

• Flask
• SQLAlchemy
• Sqlite3
• Pandas
• Matplotlib
• Sklearn
• KNN

How to run

• Run save.py and copy paste the URL in your browser.
• data.py contains the SQL commands to generate the database.
• recommendation_engine.py contains the Recommendation Engine.