{"id":19445160,"url":"https://github.com/akashkg03/spotify-recommendation-system","last_synced_at":"2026-05-07T13:05:07.431Z","repository":{"id":221508535,"uuid":"754581533","full_name":"Akashkg03/Spotify-Recommendation-System","owner":"Akashkg03","description":"This notebook analyzes Spotify song data and builds a recommendation system to suggest songs based on user listening behavior.","archived":false,"fork":false,"pushed_at":"2024-02-13T07:32:09.000Z","size":4093,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-25T08:32:33.640Z","etag":null,"topics":["clustering","matplotlib","nonnegative-matrix-factorization","pandas","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"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/Akashkg03.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":"2024-02-08T11:07:52.000Z","updated_at":"2024-02-10T04:16:09.000Z","dependencies_parsed_at":"2024-02-13T08:42:31.836Z","dependency_job_id":null,"html_url":"https://github.com/Akashkg03/Spotify-Recommendation-System","commit_stats":null,"previous_names":["akashkg03/spotify-recommendation-system"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Akashkg03/Spotify-Recommendation-System","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Akashkg03%2FSpotify-Recommendation-System","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Akashkg03%2FSpotify-Recommendation-System/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Akashkg03%2FSpotify-Recommendation-System/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Akashkg03%2FSpotify-Recommendation-System/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Akashkg03","download_url":"https://codeload.github.com/Akashkg03/Spotify-Recommendation-System/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Akashkg03%2FSpotify-Recommendation-System/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":270940719,"owners_count":24671687,"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","status":"online","status_checked_at":"2025-08-18T02:00:08.743Z","response_time":89,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["clustering","matplotlib","nonnegative-matrix-factorization","pandas","python"],"created_at":"2024-11-10T16:09:32.133Z","updated_at":"2026-05-07T13:05:07.325Z","avatar_url":"https://github.com/Akashkg03.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Spotify-Recommendation-System\n### Project Overview\n- This project analyzes Spotify song data and builds a recommendation system to suggest songs based on user listening behavior.\n\n### Dataset\n-  The dataset contains the number of songs heard by each user. Each record represents the number of times a user has listened to a particular song.\n\n### Approach\nOur approach involved the following steps:\n- Imported necessary libraries for data processing and model building.\n- Applied NMF to factorize the feature matrix into two non-negative matrices.\n- Clustered songs based on their latent factors obtained from NMF.\n- Generated recommendations based on the clustered results and user listening behavior.\n\n### Results:\nThe system successfully recommended songs based on user listening behavior.\n\n### Technologies Used:\nPython, pandas, scikit-learn, Jupyter Notebook.\n\n### Skills Demonstrated:\nClustering, Dimensionality reduction.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fakashkg03%2Fspotify-recommendation-system","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fakashkg03%2Fspotify-recommendation-system","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fakashkg03%2Fspotify-recommendation-system/lists"}