SpotifyRecSys - Spotify Music Recommendation System

Project Overview

SpotifyRecSys is a song-based recommendation system that aims to provide personalized song recommendations to users based on song features.

Table of Contents

• Project Overview
• Features
• Getting Started
  • Prerequisites
  • Installation
• Data
• Models
• Results
• Contributing
• Resources
• License
• Acknowledgements

Features

Getting Started

Prerequisites

• Python 3.x
• Required packages (list the required Python packages and their versions)
• Spotify API credentials (if applicable)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/SpotifyRecSys.git
   cd SpotifyRecSys
   

Models

• Content-based filtering
  • Cosine Similarity
  • Euclidean Distance
  • Decision Tree / Random Forest
  • Neural Networks
• Clustering Algorithms
  • K-Means Clustering
  • Hierarchical Clustering
• Dimensionality Reduction Techniques
  • Principal Component Analysis (PCA)
  • t-Distributed Stochastic Neighbor Embedding (t-SNE)
• Deep Learning Approaches
  • Convolutional Neural Networks (CNNs)
  • Autoencoders

Resources

• Spotify API and Documentation
• Github Repo
  • Spotify Recommendation System
  • Music Recommendation System
  • Spotify Music Recommender
  • Spotify Recommender Systems
  • Song Recommender System
  • Spotify Recommendation System
• Kaggle Notebook
  • Music Recommender (Bonus Streamlit app)
  • Music Recommendation System Streamlit
  • Music Recommendation System using Spotify Dataset
  • Spotify Data EDA and Music Recommendation
  • Spotify Extensive Analysis & Song Recommender
• Articles
  • Build a Song Recommendation System using Streamlit and Deploy on Heroku