Music Recommender System

Overview

This repository contains the backend and frontend components of a Music Recommender System. The backend is implemented in Python, utilizing machine learning models to recommend music based on user preferences. The frontend is implemented using Streamlit, providing a user-friendly interface to interact with the recommendation system.

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 system. It handles 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.