Director of Business Intelligence , CouponFollow
Data Engineer - Capacity Supply Chain and Provisioning, Microsoft India CoE
Data Science Consultant, Fractal Analytics
Data and Blockchain Professional
Python Recommender Systems Project - Learn to build a graph based recommendation system in eCommerce to recommend products.
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Business Objective:
There are hundreds of eCommerce websites with millions of products listed. Personalizing the content is much needed to engage the user with the platform. This is where recommendation systems come into the picture. You must have heard about some recommendation systems such as Content-Based, Collaborative filtering, etc. In recent years Graph, Learning-based Recommendation systems have witnessed fast development.
This project aims to give you a brief idea about recommendation systems and how they work. Moreover, we build a recommendation system using Graph-based learning for an eCommerce platform.
Aim:
To build a Graph-based recommender system that will recommend the best product for the users in e-commerce platforms depending on their purchase and search history
Data Overview :
The dataset contains user information over 9 attributes for an eCommerce website
Tech Stack:
Language: Python
Packages: DuckDB, pandas, Numpy
File Management: Parquet
Database Management: SQL, SQL querying
Approach:
Understand the problem statement
Extract appropriate data
Understanding user journey on an eCommerce platform.
Introducing the concept of graphs to build the recommendation engine
Understanding graphs and their types
Predict using the Recommender engine
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