{"id":23020405,"url":"https://github.com/brazer27/iris-classification","last_synced_at":"2025-09-06T11:51:43.520Z","repository":{"id":265205050,"uuid":"895445530","full_name":"Brazer27/Iris-Classification","owner":"Brazer27","description":"A Python implementation of Naive Bayes algorithm for Iris flower classification. Features include cross-validation, data preprocessing, and prediction capabilities. Built from scratch without ML libraries, achieving ~95% accuracy on the classic Iris dataset.","archived":false,"fork":false,"pushed_at":"2024-11-28T15:44:43.000Z","size":10,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-02T18:51:57.555Z","etag":null,"topics":["cross-validation","data-science","data-visualization","flower-classification","iris-dataset","machine-learning","naive-bayes","python"],"latest_commit_sha":null,"homepage":"","language":"Python","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/Brazer27.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":"2024-11-28T08:18:04.000Z","updated_at":"2024-11-28T15:44:47.000Z","dependencies_parsed_at":"2025-04-02T18:42:49.789Z","dependency_job_id":"530e8551-865d-4979-b121-907eaf00fcb0","html_url":"https://github.com/Brazer27/Iris-Classification","commit_stats":null,"previous_names":["brazer27/iris-classification"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Brazer27/Iris-Classification","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Brazer27%2FIris-Classification","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Brazer27%2FIris-Classification/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Brazer27%2FIris-Classification/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Brazer27%2FIris-Classification/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Brazer27","download_url":"https://codeload.github.com/Brazer27/Iris-Classification/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Brazer27%2FIris-Classification/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":273899838,"owners_count":25187737,"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":"2025-09-06T02:00:13.247Z","response_time":2576,"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":["cross-validation","data-science","data-visualization","flower-classification","iris-dataset","machine-learning","naive-bayes","python"],"created_at":"2024-12-15T12:13:58.466Z","updated_at":"2025-09-06T11:51:43.455Z","avatar_url":"https://github.com/Brazer27.png","language":"Python","readme":"# Iris Classification using Naive Bayes\n\nA Python implementation of the Naive Bayes algorithm for classifying Iris flowers. This project provides two implementations:\n- A comprehensive version with cross-validation and visualization\n- A simplified version focused on making predictions\n\n## Features\n- Gaussian Naive Bayes implementation from scratch\n- K-fold cross-validation\n- Performance visualization using box plots\n- Data preprocessing utilities\n- Simple interface for making predictions on new data\n\nDataset: The classic Iris dataset containing 150 samples with 4 features (sepal length, sepal width, petal length, petal width) and 3 classes of Iris flowers.\n\nAverage accuracy: ~95% using 5-fold cross-validation\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbrazer27%2Firis-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbrazer27%2Firis-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbrazer27%2Firis-classification/lists"}