Book Recommendation System

About the project:

This is a streamlit web application that can recommend various kinds of similar books based on an user interest the concept of Collaborative based filtering is used here.

• Click here to run it live on server

Project Demo:

Dataset Link:

• Dataset link

Concept used to build the model.pkl file : NearestNeighbors

1 . Load the data

2 . Initialise the value of k

3 . For getting the predicted class, iterate from 1 to total number of training data points

4 . Calculate the distance between test data and each row of training data. Here we will use Euclidean distance as our distance metric since it’s the most popular method.

5 . Sort the calculated distances in ascending order based on distance values

6 . Get top k rows from the sorted array

How to run?

STEPS:

Clone the repository

https://github.com/rachitdani/Book-Recommender-System.git

STEP 01- Create a conda environment after opening the repository

conda create -n bookenv python=3.8 -y

conda activate bookenv

STEP 02- install the requirements

pip install -r requirements.txt

# Run this file to generate the model files in the artifacts folder
notebook.ipynb

Now run,

streamlit run app.py

Contributing

Contributions are welcome! If you'd like to contribute to this project, please follow these following steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix: git checkout -b feature-name.
3. Make your changes and commit them: git commit -m 'Description of your changes'.
4. Push your changes to your fork: git push origin feature-name.
5. Create a pull request on the original repository.