Music Recommender System

Overview

"Welcome to our Music Recommendation System repository! Here, we've implemented a comprehensive solution that leverages datasets to make data preprocessing techniques, and machine learning models to deliver personalized music recommendations.

Files

1. PROJECT_SPOTIFY.ipynb: This notebook contains all the backend code for the music recommender system, including data preprocessing, feature engineering, model training, and evaluation.

2. genres_v2.csv: This CSV file is the dataset used for training the machine learning models.

3. web.py: This Python file contains the Streamlit code for the web interface of the music recommender that seamlessly integrates with our models, providing users with an intuitive platform for user interactions and displays recommendations.

4. images/: This folder contains additional images used in the web interface for enhancing user experience.

5. animations/: This folder contains animations used in the web interface for dynamic visual elements.

How to Use

1. Clone the repository and install dependencies.
2. Run the Streamlit web application using streamlit run web.py.
3. Input your music preferences such as music name and number of recommendations.
4. Explore personalized music recommendations and enjoy discovering new tracks.