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Book Recomendation

Book Recomendation

๐Ÿ“š Book Recommendation System

Movie Recommendation System

๐Ÿ“ Project Description

This is a Flask-based web application that recommends books based on popularity and ratings. The system is built using a collaborative filtering approach and primarily utilizes a popularity-based model to suggest books with the highest ratings.

๐Ÿ”— Live Demo โ€“ Book Recommendation System

๐Ÿ“‚ GitHub Repository

๐Ÿ”— Link to GitHub Repo


๐Ÿš€ How to Use

1๏ธโƒฃ Visit the homepage to see the top recommended books.
2๏ธโƒฃ Enter the book name or search criteria in the input box.
3๏ธโƒฃ Click โ€œRecommendโ€ to see the suggestions.


๐Ÿ“‚ Dataset

The project uses a book recommendation dataset from Kaggle for training and evaluation.

๐Ÿ”— Kaggle Dataset Link โ€“ (Provide actual link here)


๐Ÿ” Model Training Approach

The book recommendation system is trained using the following steps:

1๏ธโƒฃ [popular_book] โ€“ Identifies the top 50 most popular books based on ratings and votes.
2๏ธโƒฃ [stan_df] โ€“ Filters users who have rated more than 200 different books.
3๏ธโƒฃ [rating_50_book] โ€“ Selects books that have received more than 50 ratings (i.e., rated by at least 50 different users).
4๏ธโƒฃ [pt] โ€“ Creates a pivot table for rating_50_book based on ratings and votes.
5๏ธโƒฃ [similarity_s] โ€“ Computes the similarity score between books.
6๏ธโƒฃ Recommendation Function โ€“ Given a book name, the system suggests 6 most related books.


๐ŸŒ Deployment (Render)

The model is deployed online using Render.com for easy accessibility.

๐Ÿ”ง Deployment Steps:

1๏ธโƒฃ Render Setup โ€“ Create a Render.com account and set up a new โ€œWeb Serviceโ€.
2๏ธโƒฃ Connect GitHub โ€“ Link the Render service to this GitHub repository.
3๏ธโƒฃ Configure Build Commands โ€“ Review and adjust the build commands (auto-detected for Python projects).
4๏ธโƒฃ Deploy โ€“ Click โ€œDeployโ€, and Render will build & launch the app.
5๏ธโƒฃ Access โ€“ Use the Render URL to access the deployed system.


๐Ÿ”ฎ Future Enhancements

๐Ÿ“Œ User-Based Filtering โ€“ Recommend books based on individual user preferences.
๐Ÿ“Œ Content-Based Filtering โ€“ Suggest books using textual similarities in descriptions.
๐Ÿ“Œ Hybrid Model โ€“ Combine popularity-based & collaborative filtering for improved accuracy.
๐Ÿ“Œ Enhanced UI โ€“ Develop a more interactive & visually appealing interface.


๐ŸŽ‰ This project helps book lovers discover new recommendations effortlessly! ๐Ÿš€

This post is licensed under CC BY 4.0 by the author.