Amazon Product Recommendation System

As a participant in the Data Science and Machine Learning: Making Data-Driven Decisions program by MIT Institute for Data, Systems, and Society (IDSS), I undertook a project focused on the construction of a recommendation system using rank-based and collaborative filtering techniques. The project utilized the Amazon product reviews dataset, containing ratings for various electronic products. To mitigate potential bias, the dataset excluded detailed information about the products or reviews, ensuring a more objective approach in building the recommendation model.

In the contemporary landscape of information overload and consumer choices, recommender systems play a pivotal role in guiding users through the vast array of products available online. The objective of this project was to contribute to the development of a recommendation system that would analyze customers’ past ratings and derive valuable insights from Amazon product reviews. By leveraging algorithms inspired by industry leaders like Amazon, particularly item-to-item collaborative filtering, the system aimed to provide personalized product recommendations, enhancing the overall online shopping experience for consumers.

E-commerce giants such as Amazon invest significant resources in developing sophisticated recommendation models. The success of these systems lies in their ability to intelligently analyze and predict customers’ preferences, offering accurate and relevant suggestions. Through this project, I delved into the world of recommendation systems, contributing to the ongoing efforts to improve personalization in e-commerce platforms and address the challenges posed by information overload, ultimately striving to make online shopping a more tailored and enjoyable experience for users.

The code for this case study can be found in my Kaggle profile.

Skills & Tools

Data Science, Machine Learning, Recommender Systems, Collaborative Filtering Python