book-recommendation-model

Hybrid book recommendation system fusing a scikit-learn k-nearest-neighbors collaborative filtering model with a TF-IDF cosine-similarity content model, weighted-average combined and trained on the 1.1 million rating Book-Crossing dataset, served through a FastAPI backend with TTL caching, a decoupled React frontend, and Docker Compose deployment.

Building a recommendation system and deplying using streamlit

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.

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…

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