Full-stack hybrid book recommendation system combining Collaborative Filtering and Content-Based Filtering with weighted hybrid scoring, modular data pipelines, and model persistence. Deployed via Flask with responsive HTML/CSS UI and integrated CI/CD for production-ready, scalable, and interactive recommendations.

The project aims to create a Book recommendation system that predicts user interests and recommend the suitable/appropriate books to them ,using various approaches like User based Collaborative Filtering and Item based Collaborative Filtering.

Building a recommendation system and deplying using streamlit

This repo was created as a part of project submission for Microsoft Engage 2022

Made a Book Recommendation machine learning modelusing multivariable logistic regression , Used labeled dataset og approx 6000 books for visualizing features and training,testing of the model , Employed various modules of sci-kit learn ,seaborn,matplotlib and Numpy libraries in the model . Used confusion matrix to show accuracy score of the mod…