https://github.com/prachipatel15/book-recommender-system

This Streamlit app will recommend top 50 books as well as top 5 similar books similar to user entered,
https://github.com/prachipatel15/book-recommender-system

collaborative-filtering python recommendation-system streamlit

Last synced: 4 months ago
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This Streamlit app will recommend top 50 books as well as top 5 similar books similar to user entered,

• Host: GitHub
• URL: https://github.com/prachipatel15/book-recommender-system
• Owner: PrachiPatel15
• Created: 2022-08-22T10:25:42.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2022-12-19T18:42:31.000Z (over 3 years ago)
• Last Synced: 2025-09-25T07:33:31.932Z (9 months ago)
• Topics: collaborative-filtering, python, recommendation-system, streamlit
• Language: Jupyter Notebook
• Homepage: https://github.com/PrachiPatel15/Book-Recommender-system
• Size: 17.3 MB
• Stars: 1
• Watchers: 1
• Forks: 3
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

          
## Book-Recommender-System - Overview

- This project has two main objectives.

- First objective is to show **Top 20** books of taken dataset.

  - This is Popularity Based Recommender System

- And the second objective is to recommend 5 books to user entered book.

  - This is Collaborative Filtering Type of recommendation

  - And in this project the major concern was to focus on user ratings and make recommendation based on that

- At the beginning data merging and data cleaning was performed.

- **Streamlit**'framework was being used and web app was created.

- The web app was then deployed on **Streamlit Cloud**.

- Link: https://prachipatel15-book-recommender-system-app-batk0m.streamlit.app/

## Data

https://www.kaggle.com/datasets/arashnic/book-recommendation-dataset

- There is 3 csv files available for the project.

  - Books.csv

  - Ratings.csv

  - Users.csv

- After downloading 3 of them, you can start cleaning and building a model.

- In this project, **Cosine Similarity** was used for building collaborative filtering recommender system.

 

## Code and Resources Used

- ***Python Version:*** 3.9

- ***Packages:*** Streamlit

- ***For Web Framework Requirements:*** ```pip install -r requirements.txt```

## Conclusion 

- Both Popularity and Collaborative firterling are working at some extend.

- In collaborative filtering, cosine similarity pays huge role and it's results were more consistent.

- Here is some of the pictures from the web app.

- ![](https://github.com/PrachiPatel15/Book-Recommender-System/blob/main/webapp-1.png)

- ![](https://github.com/PrachiPatel15/Book-Recommender-System/blob/main/webapp-2.png)