{"id":27278945,"url":"https://github.com/sedatdikbas/traditional-machine-learning","last_synced_at":"2026-04-28T08:03:14.568Z","repository":{"id":287367860,"uuid":"964509309","full_name":"SedatDikbas/traditional-machine-learning","owner":"SedatDikbas","description":"Geleneksel Makine Öğrenmesi Yöntemleri ile Çalışmalarım","archived":false,"fork":false,"pushed_at":"2025-04-11T10:30:37.000Z","size":347,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-11T17:51:55.182Z","etag":null,"topics":["classification","confusion-matrix","data-analysis","data-visualization","decision-tree","machine-learning","naive-bayes","python","random-forest","svm","traditional-machine-learning"],"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/SedatDikbas.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":"2025-04-11T10:23:13.000Z","updated_at":"2025-04-11T11:22:51.000Z","dependencies_parsed_at":"2025-04-11T12:00:24.809Z","dependency_job_id":"8a1909b0-484f-4a41-bd87-e32492c21e3e","html_url":"https://github.com/SedatDikbas/traditional-machine-learning","commit_stats":null,"previous_names":["sedatdikbas/traditionalmachinelearning"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/SedatDikbas/traditional-machine-learning","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SedatDikbas%2Ftraditional-machine-learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SedatDikbas%2Ftraditional-machine-learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SedatDikbas%2Ftraditional-machine-learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SedatDikbas%2Ftraditional-machine-learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SedatDikbas","download_url":"https://codeload.github.com/SedatDikbas/traditional-machine-learning/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SedatDikbas%2Ftraditional-machine-learning/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32371673,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-27T20:07:02.737Z","status":"online","status_checked_at":"2026-04-28T02:00:07.250Z","response_time":56,"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":["classification","confusion-matrix","data-analysis","data-visualization","decision-tree","machine-learning","naive-bayes","python","random-forest","svm","traditional-machine-learning"],"created_at":"2025-04-11T17:46:05.748Z","updated_at":"2026-04-28T08:03:14.550Z","avatar_url":"https://github.com/SedatDikbas.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Traditional Machine Learning Classification on Balanced Dataset\n\nBu projede, dengelenmiş bir veri kümesi (`balanced_update_data_dt.csv`) üzerinde çeşitli geleneksel makine öğrenmesi sınıflandırma algoritmaları kullanılarak performans karşılaştırması yapılmıştır.\n\n## Kullanılan Algoritmalar\n\n- Decision Tree (Karar Ağacı)\n- Support Vector Machine (SVM)\n- Naive Bayes\n- Random Forest\n\n## Adımlar\n\n1. **Veri Yükleme ve Hazırlık:**  \n   Veri kümesi yüklenmiş, `class` sütunu hedef değişken olarak ayrılmıştır. Eğitim ve test kümeleri %80-%20 oranında bölünmüştür.\n\n2. **Sınıf Dağılımının Görselleştirilmesi:**  \n   Sınıf dağılımı çubuk grafikle gösterilerek dengenin sağlandığı doğrulanmıştır.\n\n3. **Korelasyon Isı Haritası:**  \n   Hedef değişken ile öznitelikler arasındaki ilişki ısı haritası ile görselleştirilmiştir.\n\n4. **Model Eğitimi ve Değerlendirme:**  \n   Her bir model, eğitim verisi ile eğitilip test verisi üzerinde değerlendirilmiştir. Değerlendirme metrikleri:\n   - Accuracy (Doğruluk)\n   - Precision, Recall, F1-Score\n   - Confusion Matrix (Karışıklık Matrisi)\n\n5. **Görselleştirme:**  \n   Her model için karışıklık matrisi ayrı ayrı ısı haritası olarak çizdirilmiştir.\n\n## Gereksinimler\n\n```bash\npip install pandas matplotlib seaborn scikit-learn\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsedatdikbas%2Ftraditional-machine-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsedatdikbas%2Ftraditional-machine-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsedatdikbas%2Ftraditional-machine-learning/lists"}