{"id":17347579,"url":"https://github.com/janasunrise/ml-guide-and-implementation","last_synced_at":"2026-03-10T11:05:30.645Z","repository":{"id":52936670,"uuid":"318412241","full_name":"janaSunrise/ML-guide-and-implementation","owner":"janaSunrise","description":"This repository contains the predictions, and plots  for the datasets included in the scikit learn library  by default and also some other datasets from kaggle or other sources.","archived":false,"fork":false,"pushed_at":"2023-02-19T15:26:37.000Z","size":17821,"stargazers_count":3,"open_issues_count":1,"forks_count":4,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-21T21:10:32.022Z","etag":null,"topics":["machine-learning","ml","python3","scikit","scikit-learn","scikitlearn-machine-learning","sklearn"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/janaSunrise.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":".github/CODEOWNERS","security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2020-12-04T05:21:45.000Z","updated_at":"2021-07-15T08:04:35.000Z","dependencies_parsed_at":"2025-04-14T21:01:01.524Z","dependency_job_id":"d1238c37-1ae5-49bd-977e-a74ac2ccd2df","html_url":"https://github.com/janaSunrise/ML-guide-and-implementation","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/janaSunrise/ML-guide-and-implementation","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/janaSunrise%2FML-guide-and-implementation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/janaSunrise%2FML-guide-and-implementation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/janaSunrise%2FML-guide-and-implementation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/janaSunrise%2FML-guide-and-implementation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/janaSunrise","download_url":"https://codeload.github.com/janaSunrise/ML-guide-and-implementation/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/janaSunrise%2FML-guide-and-implementation/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30331654,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-10T05:25:20.737Z","status":"ssl_error","status_checked_at":"2026-03-10T05:25:17.430Z","response_time":106,"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":["machine-learning","ml","python3","scikit","scikit-learn","scikitlearn-machine-learning","sklearn"],"created_at":"2024-10-15T16:49:19.979Z","updated_at":"2026-03-10T11:05:30.621Z","avatar_url":"https://github.com/janaSunrise.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Sklearn dataset predictions\n\nThis repository contains the predictions, and plots \nfor the datasets included in the scikit learn library \nby default and also some other datasets from kaggle or other sources.\n\n## 🛠️ Tech stack used\n\n- `Pandas`: For the data manipulation\n- `Matplotlib`: Doing plotting\n- `Numpy`: As a dependency for Pandas\n- `Scikit-learn`: The most important library for ML\n\n## ❓ How to run this locally\n\n### NOTE:\n\nBefore cloning this repo, you need to ensure you have [GIT LFS](https://git-lfs.github.com/) \ninstalled on your local system. Because this repository contains several `*.csv` files, \nwhich are quite large and aren't accepted by github directly. Sorry for this inconvience.\n\n### Steps for running locally:\n\n- Run for Testing\n\n  As the virtualenv for separating the dependencies, I've gone with \n  pipenv for it. It's really modular and easy to use.\n  \n  Use `pipenv shell` to activate the virtualenv and then execute the python\n  commands to run the files and display accuracy.\n\n- Run for development and contributing\n\n  We also encourage people to support this repository by contributing, and keeping it alive.\n  But note that we follow certain steps to ensure code is clean, organized and readable using\n  linting with `flake8`. We also encourage using pre-commit for pushing clean code.\n\n  Steps to set up:\n  - Install dependencies: `pipenv update -d`\n  - Setup pre commit: `pipenv run precommit`\n  - After changes, try linting: `pipenv run lint`\n\n## Datasets implemented\n\n### Diabetes: \n\nThis dataset consists of 9 columns.\nThe target value which has to be predicted is `diabetes`\nThis is a classifier problem, where the value of diabetes in boolean,\nbut in integer format.\n\nAlgorithm used for the problem: `GradientBoostingClassifier`\n\nAccuracy achieved: `0.74`\n\n## 🤝 Contributing\n\nContributions, issues and feature requests are welcome. After cloning \n\u0026 setting up project locally, you can just submit a PR to this \nrepo and it will be deployed once it's accepted. The contributing \nfile can be found [here](https://github.com/janaSunrise/sklearn-datasets-implementation/blob/main/CONTRIBUTING.md).\n\n⚠️ It’s good to have descriptive commit messages, or PR titles so that other contributors can understand about your commit or the PR Created.\nRead [conventional commits](https://www.conventionalcommits.org/en/v1.0.0-beta.3/) before making the commit message.\n\nAnd, for contributions we have a Branch named `dev`, So if you're interested in contributing, \nPlease contribute to that branch instead of the `main` branch.\n\n## 😁 Maintainers\n\nWe have 2 maintainers for this project as of now:\n- [Sunrit Jana](https://github.com/janaSunrise)\n- [Rohith MVK](https://github.com/Rohith04MVK)\n\n## 🙌 Show your support\n\nBe sure to leave a ⭐️ if you like the project, and also be sure to contribute, if you're interested!\n\n\u003cdiv align=\"center\"\u003e\n\nMade by Sunrit Jana with ❤️\n\n\u003c/div\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjanasunrise%2Fml-guide-and-implementation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjanasunrise%2Fml-guide-and-implementation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjanasunrise%2Fml-guide-and-implementation/lists"}