Music Recommendation System

This project is a simple music recommendation system that suggests songs based on their energy levels. It uses a dataset from Spotify and calculates song similarities to provide recommendations.

Table of Contents

• Introduction
• Prerequisites
• Usage
• Features
• Contributing

Introduction

This music recommendation system is designed to help users discover songs with similar energy levels to their favorite tracks. It uses a dataset of songs from Spotify and calculates song similarities based on their energy attributes.

Prerequisites

Before you can run the project, you'll need to have the following dependencies and tools installed:

• Python 3.x
• pandas
• numpy
• scikit-learn
• pickle (Python module)

You can install these dependencies using pip:

pip install pandas numpy scikit-learn

Usage

recommended_songs = recommend_songs("Song name among the data set", spotify, similarities)
print(recommended_songs)

Example

recommended_songs = recommend_songs("More Hearts Than Mine", spotify, similarities) recommended_songs

Output

['My Heart Went Oops',
'Friday 13th (feat. Octavian)',
'Cornelia Street - Live From Paris',
'Welcome to Chilis',
'Demon Time (Lil Yachty feat. Draft Day)']

Features

Recommends songs based on energy levels.
Uses a custom similarity metric based on energy attributes.
Preprocesses the dataset to improve recommendation accuracy.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.