{"id":20345330,"url":"https://github.com/andrewjmack/cryptoclustering","last_synced_at":"2026-04-30T08:40:19.374Z","repository":{"id":246684904,"uuid":"821845818","full_name":"andrewjmack/CryptoClustering","owner":"andrewjmack","description":"The purpose of this project is to utilize knowledge of Python and unsupervised learning to predict if cryptocurrencies are affected by 24-hour or 7-day price changes. Methods for analysis include K-Means clustering and dimensional reduction  through Principal Component Analysis (\"PCA\").","archived":false,"fork":false,"pushed_at":"2024-07-23T22:49:57.000Z","size":1257,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-04T15:47:45.110Z","etag":null,"topics":["jupyter-notebook","pandas","python","scikit-learn"],"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/andrewjmack.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":"2024-06-29T15:47:44.000Z","updated_at":"2024-07-23T22:50:00.000Z","dependencies_parsed_at":"2024-06-29T17:25:33.261Z","dependency_job_id":"f0137cd1-bcec-4112-af1b-0f3fe66ace8d","html_url":"https://github.com/andrewjmack/CryptoClustering","commit_stats":null,"previous_names":["andrewjmack/cryptoclustering"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/andrewjmack/CryptoClustering","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andrewjmack%2FCryptoClustering","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andrewjmack%2FCryptoClustering/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andrewjmack%2FCryptoClustering/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andrewjmack%2FCryptoClustering/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/andrewjmack","download_url":"https://codeload.github.com/andrewjmack/CryptoClustering/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andrewjmack%2FCryptoClustering/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":263966169,"owners_count":23536814,"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":["jupyter-notebook","pandas","python","scikit-learn"],"created_at":"2024-11-14T22:07:49.552Z","updated_at":"2026-04-30T08:40:19.338Z","avatar_url":"https://github.com/andrewjmack.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# CryptoClustering\nUniv of Denver: Data Analytics | July 2024 | Andrew Mack\n\n### Summary\nThe purpose of this project is to utilize knowledge of Python and unsupervised learning to predict if cryptocurrencies are affected by 24-hour or 7-day price changes.\n\nThe following stages were involved:\n- Finding the best value for k after scaling a dataframe\n- Clustering the cryptocurrencies with K-means using the original scaled data\n- Optimizing the clusters with Principal Component Analysis (\"PCA\")\n- Finding the best value for k using the PCA data\n- Cluster the cryptocurrencies with K-means using the PCA data\n\n### Contents\nThis repository includes the original data in a .csv file and a Jupyter Notebook in which the .csv data was transformed and the analysis occurred:\n-   Crypto_Clustering.ipynb\n-   Resources/crypto_market_data.csv\n-   README.md\n\n### References\n- Data for this dataset was generated by edX Boot Camps LLC, and is intended for educational purposes only.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fandrewjmack%2Fcryptoclustering","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fandrewjmack%2Fcryptoclustering","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fandrewjmack%2Fcryptoclustering/lists"}