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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.