Music Recommendation System

This project implements a simple music recommendation system using machine learning techniques. It processes a large dataset of music tracks provided by Spotify.

Features

1. Data Preprocessing: Transforms raw music track data into a format suitable for machine learning algorithms.
2. Feature Engineering: Applies One-Hot Encoding for categorical variables and TF-IDF for genre information.
3. Recommendation System: Implements a nearest neighbors approach for song recommendations.
4. Streamlit app: A demo is available on Streamlit.

Technologies Used

• Python 3.x
• Pandas: For data manipulation and analysis
• Scikit-learn: For machine learning algorithms (PCA, K-means, preprocessing)
• Plotly: For interactive data visualization

Getting Started

1. Clone this repository
2. Install the required packages:

Data Source

Dataset from Kaggle

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Test it on Streamlit