Spotify Song Recommender

This repository contains code for analyzing and recommending songs based on Spotify data. The analysis includes clustering genres, visualizing feature correlations, and recommending songs similar to a given list.

Prerequisites

Before running the code, make sure you have the following:

Spotify Developer credentials (Client ID and Client Secret)
Spotify API access
Python environment with necessary packages (see requirements.txt)

Getting Started

Clone the repository:

git clone https://github.com/your-username/spotify-song-recommender.git
cd spotify-song-recommender

Install dependencies:
```
pip install -r requirements.txt
```

Set up your Spotify Developer credentials:

# Add your Spotify Client ID and Client Secret as environment variables
export SPOTIFY_CLIENT_ID='your_client_id'
export SPOTIFY_CLIENT_SECRET='your_client_secret'

Run the main script:
```
python main.py
```

Project Structure

main.py: Main script for running the song recommender.
data_preprocessing.py: Data loading and preprocessing.
analysis_visualization.py: Analysis and visualization functions.
song_recommender.py: Song recommendation functions.
Spotify_Data/: Directory containing Spotify data files.

Usage

Follow the steps in main.py to analyze the Spotify data, visualize correlations, and generate song recommendations based on input songs.

Feel free to customize and explore the code to suit your preferences and use cases.

Results

After running the recommender with sample input songs ('lose yourself' from 2002 and 'brand new' from 2023), the output suggests similar songs: