{"id":29204880,"url":"https://github.com/gurupatil0003/algorithm","last_synced_at":"2026-02-04T00:02:57.637Z","repository":{"id":289627116,"uuid":"971253855","full_name":"Gurupatil0003/Algorithm","owner":"Gurupatil0003","description":"Clustering techniques like K-Means, EM Algorithm, and Apriori are widely used in data analysis for various purposes. K-Means is commonly used for customer segmentation, where it groups customers with similar behaviors or characteristics.","archived":false,"fork":false,"pushed_at":"2025-04-24T10:56:25.000Z","size":176,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-07-02T15:51:05.178Z","etag":null,"topics":["github","ml","mlops-workflow","mongodb"],"latest_commit_sha":null,"homepage":"","language":null,"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/Gurupatil0003.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,"zenodo":null}},"created_at":"2025-04-23T08:40:00.000Z","updated_at":"2025-05-23T06:06:01.000Z","dependencies_parsed_at":"2025-04-24T08:35:43.264Z","dependency_job_id":"f2221e34-9b4c-4714-aa42-8e4a4e69e13b","html_url":"https://github.com/Gurupatil0003/Algorithm","commit_stats":null,"previous_names":["gurupatil0003/algorithm"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Gurupatil0003/Algorithm","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Gurupatil0003%2FAlgorithm","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Gurupatil0003%2FAlgorithm/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Gurupatil0003%2FAlgorithm/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Gurupatil0003%2FAlgorithm/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Gurupatil0003","download_url":"https://codeload.github.com/Gurupatil0003/Algorithm/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Gurupatil0003%2FAlgorithm/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29062483,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-03T23:14:54.203Z","status":"ssl_error","status_checked_at":"2026-02-03T23:14:50.873Z","response_time":96,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":["github","ml","mlops-workflow","mongodb"],"created_at":"2025-07-02T15:37:13.939Z","updated_at":"2026-02-04T00:02:57.620Z","avatar_url":"https://github.com/Gurupatil0003.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"\n\n```\nhttps://in.images.search.yahoo.com/search/images;_ylt=Awrx_3uA5gloLgIAoTq7HAx.;_ylu=Y29sbwNzZzMEcG9zAzEEdnRpZAMEc2VjA3BpdnM-?p=aprior+algorithm\u0026fr2=piv-web\u0026type=E211IN714G0\u0026fr=mcafee#id=355\u0026iurl=https%3A%2F%2Frailsware.com%2Fblog%2Fwp-content%2Fuploads%2F2018%2F09%2Fillustration-9.jpg\u0026action=click\n```\n\n![Flowchart-of-Apriori-algorithm](https://github.com/user-attachments/assets/e8627b30-a137-4d45-9ddc-052974f5b308)\n\n# EM ALgorithm\n``` python\n1. **Guess initial values** for the hidden parts of your data (e.g., cluster centers or parameters).\n     ↓\n2. Repeat until things stop improving:\n     a. **Estimate**: Using your current guesses, figure out the missing information (like how likely each data point belongs to each group).\n     b. **Maximize**: Update your guesses based on the new information. Adjust the hidden parts of your data to better fit the observed data.\n     ↓\n3. You now have the best estimates for the hidden parts and the model.\n```\n\n# K means\n```pytohn\n1. Pick how many groups (K) you want.\n     ↓\n2. Randomly place K “center” points in your data space.\n     ↓\n3. Repeat until things stop changing much:\n     a. **Sort**: For each data item, find which center it’s closest to and put it in that group.\n     b. **Move**: For each group, shift its center to the middle of the items in that group.\n     ↓\n4. You now have K groups and their centers—clustering complete!\n```python\n# Apriori\n\n\u003cimg src=\"https://intellipaat.com/blog/wp-content/uploads/2019/05/picturemessage_fd2uio5j.1rj.png\" alt=\"Description\" width=\"4000\" style=\"border-radius: 10px; margin-top: 10px;\"\u003e\n```pytohn\n1. **Start with individual items**: Look at the items in each transaction and see how often they appear together.\n     ↓\n2. **Find frequent item sets**: Identify which combinations of items appear frequently (above a certain threshold).\n     ↓\n3. **Generate larger item sets**: Combine the frequent item sets to make larger combinations and check if they appear often.\n     ↓\n4. **Repeat** until no more large item sets are frequent.\n     ↓\n5. **Find associations**: Based on frequent item sets, find rules like \"If you buy X, you might also buy Y\".\n\n\n```\n\n\n\n\n\n### Further Reading \u0026 Interactive Notebooks\n\n\n\n| Topic         | Medium Article                                                                                                                   | Google Colab                                                                                                                 |\n|---------------|----------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------|\n| K Means       | [\u003cimg src=\"https://img.shields.io/badge/Medium-Read%20on%20Medium-000000?logo=medium\u0026logoColor=white\" alt=\"Read on Medium\"/\u003e](https://medium.com/@gurupatil327/k-means-clustering-algorithm-f642cae2f627)       | [\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e](https://colab.research.google.com/drive/1dIdZpkMgT6XRfPMNmQ5MSfO3O_0a7m6O?usp=sharing) |\n| EM Algorithm  | [\u003cimg src=\"https://img.shields.io/badge/Medium-Read%20on%20Medium-000000?logo=medium\u0026logoColor=white\" alt=\"Read on Medium\"/\u003e](https://medium.com/@gurupatil327/introduction-to-em-algorithm-in-ml-dfe2f1ea079c)           | [\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e](https://colab.research.google.com/drive/1TPiUsaPO-V-NBEqVx7L5oXTDIsNh3NIp?usp=sharing)  |\n| Apriori       | [\u003cimg src=\"https://img.shields.io/badge/Medium-Read%20on%20Medium-000000?logo=medium\u0026logoColor=white\" alt=\"Read on Medium\"/\u003e](https://medium.com/@gurupatil327/association-rule-mining-concept-and-implementation-0d2217fd1d1d)       | [\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e](https://colab.research.google.com/drive/1Mt71U44NfFJJo0YErwH2kvRtXqJOHzOi?usp=sharing) |\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgurupatil0003%2Falgorithm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgurupatil0003%2Falgorithm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgurupatil0003%2Falgorithm/lists"}