Music Recommendation System

This project is a personalized music recommendation system that integrates machine learning models with the Spotify API to provide tailored music recommendations and playlist management. Built as a user-friendly web application using Streamlit, the system allows users to discover new music and organize playlists seamlessly.

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Full Report

For a detailed breakdown of this project, please refer to the full report in this PDF file.
Video Presentation: https://www.youtube.com/watch?v=biaQ1d6yNmo

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Features

• Integration with Spotify API
  Users can log in with their Spotify accounts to access personalized recommendations and manage playlists directly in their library.

• Tailored Music Recommendations
  Generate song suggestions based on moods or similarity to user-selected tracks using pre-trained machine learning models.

• Playlist Management
  Create or update Spotify playlists with recommended songs in just a few clicks.

• Interactive Web Application
  Built using Streamlit for a clean and intuitive user interface.

• Secure Authentication
  Utilizes Spotipy and OAuth2 for secure user authentication and token management.

• Scalable Deployment
  Hosted on Streamlit Cloud for easy accessibility and reliable performance.

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Getting Started

Prerequisites

Ensure you have the following installed:

• Python 3.11
• Spotify Developer Account (to generate API credentials)

Installation

1. Clone the repository:

   git clone https://github.com/gabriel-ferreira-life/Spotify-Data-Project.git

2. Install dependencies:

   pip install -r requirements.txt

3. Set up your Spotify Developer credentials:

   • Create an app on the Spotify Developer Dashboard.
   • Add your Client ID, Client Secret, and Redirect URI to a .toml file:

     [spotify]
     client_id = "your_client_id"
     client_secret = "your_client_secret"
     redirect_uri = "your_redirect_uri"

   • Place this .toml file in a .streamlit folder at the root of your project.
   • Make sure to include .streamlit/secrets.toml in your .gitignore file to keep it secure.

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Usage

1. Run the Application
   Start the Streamlit app:

   streamlit run app.py

2. Authenticate with Spotify

   • Log in with your Spotify account when prompted.
   • Authorize the app to access your Spotify data.

3. Discover Music

   • Choose a recommendation method: similarity-based or mood-based.
   • Explore suggested tracks and create playlists in your Spotify library.

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Technologies Used

• Programming Language: Python
• Framework: Streamlit
• API Client: Spotipy
• Machine for Recommendation: Pre-trained clustering and similarity models
• Deployment: Streamlit Cloud