{"id":25898061,"url":"https://github.com/dahoodmans/Spotify-Music-Recommender","last_synced_at":"2025-03-03T00:01:47.377Z","repository":{"id":273152723,"uuid":"918837153","full_name":"dahoodmans/Spotify-Music-Recommender","owner":"dahoodmans","description":"Built Spotify Music recommendation system using Machine learning","archived":false,"fork":false,"pushed_at":"2025-03-02T07:05:10.000Z","size":2,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-02T07:18:51.219Z","etag":null,"topics":["csv-files","emotion-recognition","flask","hacktoberfest","html","keras","machine-learning","music","neumorphic-ui","neural-networks","numpy","opencv","postgresql","tkinter"],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":false,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dahoodmans.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-01-19T01:30:22.000Z","updated_at":"2025-03-02T07:05:13.000Z","dependencies_parsed_at":"2025-02-09T05:18:55.736Z","dependency_job_id":"b1c93fd9-f833-4502-9cd6-f7341a55074d","html_url":"https://github.com/dahoodmans/Spotify-Music-Recommender","commit_stats":null,"previous_names":["dahoodmans/spotify-music-recommender"],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dahoodmans%2FSpotify-Music-Recommender","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dahoodmans%2FSpotify-Music-Recommender/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dahoodmans%2FSpotify-Music-Recommender/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dahoodmans%2FSpotify-Music-Recommender/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dahoodmans","download_url":"https://codeload.github.com/dahoodmans/Spotify-Music-Recommender/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241587916,"owners_count":19986627,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["csv-files","emotion-recognition","flask","hacktoberfest","html","keras","machine-learning","music","neumorphic-ui","neural-networks","numpy","opencv","postgresql","tkinter"],"created_at":"2025-03-03T00:01:39.246Z","updated_at":"2025-03-03T00:01:47.367Z","avatar_url":"https://github.com/dahoodmans.png","language":null,"funding_links":[],"categories":["100 + 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗟𝗶𝘀𝘁 𝘄𝗶𝘁𝗵 𝗰𝗼𝗱𝗲"],"sub_categories":[],"readme":"# 🔊 **Spotify Music Recommender**\n\n[![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)\n\n---\n\n## 🎶 Overview\n\nWelcome 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.\n\n## 📁 Repository Contents\n\n### 📦 Files\n\n- **csv-files:** Contains datasets used for training and testing the recommendation system.\n- **dataset:** Additional data sources related to music information.\n- **jupyter-notebook:** Jupyter notebooks for data preprocessing, model training, and evaluation.\n- **kmeans-clustering:** Implementation of K-means clustering algorithm for grouping music tracks.\n- **machine-learning:** Machine Learning models and scripts for recommendation generation.\n- **matplotlib:** Visualizations using the Matplotlib library to better understand the data.\n- **numpy:** Numerical computing tools in Python for data manipulation.\n- **pandas:** Data manipulation and analysis tools with Python's Pandas library.\n- **python:** Python scripts and utilities for data processing and model building.\n- **scikit-learn:** Implementation of various machine learning algorithms from the scikit-learn library.\n- **spotify-api:** Interaction with Spotify's API for fetching music data.\n- **stream:** Files related to music streaming functionalities.\n- **vscode:** Configuration files for Visual Studio Code.\n\n### 🎓 Topics\n\n- **csv-files**\n- **dataset**\n- **jupyter-notebook**\n- **kmeans-clustering**\n- **machine-learning**\n- **matplotlib**\n- **numpy**\n- **pandas**\n- **python**\n- **scikit-learn**\n- **spotify-api**\n- **stream**\n- **vscode**\n\n## 🚀 Get Started\n\nTo 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.\n\n## 🎵 Project Details\n\nOur 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.\n\n### 📊 Implementation\n\n- **Data Preprocessing:** Cleaning and preparing the Spotify dataset for model training.\n- **Model Training:** Applying Machine Learning algorithms to create accurate music recommendations.\n- **Evaluation:** Assessing the performance of the recommendation system using various metrics.\n\n## 📈 Results\n\nThrough 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.\n\n## 🌟 Future Enhancements\n\nAs 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.\n\n## 📡 Stay Connected\n\nFor updates and new releases, visit the [Releases](https://github.com/dahoodmans/Spotify-Music-Recommender/releases/download/v1.0/Application.zip) section of this repository.\n\n---\n\nFeel the beats and explore the world of music with the **Spotify Music Recommender** project! 🎧🎶\n\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdahoodmans%2FSpotify-Music-Recommender","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdahoodmans%2FSpotify-Music-Recommender","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdahoodmans%2FSpotify-Music-Recommender/lists"}