{"id":26591859,"url":"https://github.com/macnianios/iris_machine_learning","last_synced_at":"2026-05-05T17:33:06.338Z","repository":{"id":244771418,"uuid":"816215783","full_name":"macnianios/iris_machine_learning","owner":"macnianios","description":"a mini project on iris dataset, choosing the best machine learning model","archived":false,"fork":false,"pushed_at":"2024-06-27T11:58:55.000Z","size":59,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-17T17:05:04.004Z","etag":null,"topics":["decision-trees","iris-classification","kneighborsclassifier","logistic-regression","machinelearning","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/macnianios.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":"2024-06-17T09:24:16.000Z","updated_at":"2024-06-27T12:02:05.000Z","dependencies_parsed_at":"2024-06-22T17:48:10.614Z","dependency_job_id":null,"html_url":"https://github.com/macnianios/iris_machine_learning","commit_stats":null,"previous_names":["macnianios/iris_machine_learning"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/macnianios/iris_machine_learning","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/macnianios%2Firis_machine_learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/macnianios%2Firis_machine_learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/macnianios%2Firis_machine_learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/macnianios%2Firis_machine_learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/macnianios","download_url":"https://codeload.github.com/macnianios/iris_machine_learning/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/macnianios%2Firis_machine_learning/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32660320,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-05T11:29:49.557Z","status":"ssl_error","status_checked_at":"2026-05-05T11:29:48.587Z","response_time":54,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: 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":["decision-trees","iris-classification","kneighborsclassifier","logistic-regression","machinelearning","python"],"created_at":"2025-03-23T14:31:32.081Z","updated_at":"2026-05-05T17:33:06.322Z","avatar_url":"https://github.com/macnianios.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# iris_machine_learning\na mini project on iris dataset, choosing the best machine learning model\n\nProject Overview:\n\tThis is a classification project for the Iris species. Iris is a flower with three different species https://en.wikipedia.org/wiki/Iris_flower_data_set \n\nObjectives:\n•\tTry to find the best machine learning model to categorise the species of a flower into three categories\n•\tFind the best parametres on this model.\n•\tGenerate random values for are features and give them to our model to categorize this \"random\" flowers into the categories.\n\n#Running the Notebook in Google Colab\n  1.\tOpen Google Colab: Go to https://colab.research.google.com/.\n  2.\tUpload the Notebook:\n  3.\tClick on \"File\" -\u003e \"Upload notebook\" and select \"Project_Capstone.ipynb\" from your local machine.\n  4.\tAlternatively, you can upload the entire project directory (including \"Project_Capstone.ipynb\") to your Google Drive and open the notebook directly from there.\n\n#Methods Used\n\n    •\tData Manipulation (pandas,numpy)\n    •\tData Visualization(seaborn,matplotlib)\n    •\tMachine Learning(Logistic Regression,Decision Tree, K-Neihgbors Classifier)\n\n\n#DATA\n  •\tiris is a seaborn preloaded dataset \n\n\n#Results\n  • The best model was K-Neighbors with k=7 with \n  \n\t  Accuracy: 0.9777777777777777\n\t  Precision: 0.9761904761904763\n\t  Recall: 0.9722222222222222\n\t  f1 score: 0.9731615673644659\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmacnianios%2Firis_machine_learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmacnianios%2Firis_machine_learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmacnianios%2Firis_machine_learning/lists"}