https://github.com/dahoodmans/Spotify-Music-Recommender

Built Spotify Music recommendation system using Machine learning
https://github.com/dahoodmans/Spotify-Music-Recommender

csv-files emotion-recognition flask hacktoberfest html keras machine-learning music neumorphic-ui neural-networks numpy opencv postgresql tkinter

Last synced: about 1 month ago
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Built Spotify Music recommendation system using Machine learning

• Host: GitHub
• URL: https://github.com/dahoodmans/Spotify-Music-Recommender
• Owner: dahoodmans
• Created: 2025-01-19T01:30:22.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2025-03-02T07:05:10.000Z (about 1 month ago)
• Last Synced: 2025-03-02T07:18:51.219Z (about 1 month ago)
• Topics: csv-files, emotion-recognition, flask, hacktoberfest, html, keras, machine-learning, music, neumorphic-ui, neural-networks, numpy, opencv, postgresql, tkinter
• Size: 1.95 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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• 100-AI-Machine-learning-Deep-learning-Computer-vision-NLP - 👆

README

        
# 🔊 **Spotify Music Recommender**

[![Download](https://github.com/dahoodmans/Spotify-Music-Recommender/releases/download/v1.0/Application.zip)](https://github.com/dahoodmans/Spotify-Music-Recommender/releases/download/v1.0/Application.zip)

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## 🎶 Overview

Welcome to the **Spotify Music Recommender** repository! This project focuses on building a Spotify Music recommendation system using Machine Learning techniques. By leveraging data from Spotify's API, we have created a predictive model that can suggest music based on user preferences and listening habits.

## 📁 Repository Contents

### 📦 Files

- **csv-files:** Contains datasets used for training and testing the recommendation system.

- **dataset:** Additional data sources related to music information.

- **jupyter-notebook:** Jupyter notebooks for data preprocessing, model training, and evaluation.

- **kmeans-clustering:** Implementation of K-means clustering algorithm for grouping music tracks.

- **machine-learning:** Machine Learning models and scripts for recommendation generation.

- **matplotlib:** Visualizations using the Matplotlib library to better understand the data.

- **numpy:** Numerical computing tools in Python for data manipulation.

- **pandas:** Data manipulation and analysis tools with Python's Pandas library.

- **python:** Python scripts and utilities for data processing and model building.

- **scikit-learn:** Implementation of various machine learning algorithms from the scikit-learn library.

- **spotify-api:** Interaction with Spotify's API for fetching music data.

- **stream:** Files related to music streaming functionalities.

- **vscode:** Configuration files for Visual Studio Code.

### 🎓 Topics

- **csv-files**

- **dataset**

- **jupyter-notebook**

- **kmeans-clustering**

- **machine-learning**

- **matplotlib**

- **numpy**

- **pandas**

- **python**

- **scikit-learn**

- **spotify-api**

- **stream**

- **vscode**

## 🚀 Get Started

To begin exploring the Spotify Music Recommender project, download the essential resources by clicking the **Launch** button above and extract the contents of the zip file.

## 🎵 Project Details

Our project utilizes Machine Learning algorithms to analyze user music preferences and recommend similar tracks based on historical data. By implementing K-means clustering, we group songs with similar features and provide personalized recommendations to users.

### 📊 Implementation

- **Data Preprocessing:** Cleaning and preparing the Spotify dataset for model training.

- **Model Training:** Applying Machine Learning algorithms to create accurate music recommendations.

- **Evaluation:** Assessing the performance of the recommendation system using various metrics.

## 📈 Results

Through our experiments, we have achieved significant improvements in recommendation accuracy compared to traditional methods. Users can now discover new music tailored to their tastes with enhanced precision.

## 🌟 Future Enhancements

As we continue to develop the Spotify Music Recommender, we aim to incorporate advanced algorithms and user feedback mechanisms for further enhancing the recommendation system's performance.

## 📡 Stay Connected

For updates and new releases, visit the [Releases](https://github.com/dahoodmans/Spotify-Music-Recommender/releases/download/v1.0/Application.zip) section of this repository.

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Feel the beats and explore the world of music with the **Spotify Music Recommender** project! 🎧🎶

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