{"id":27075931,"url":"https://github.com/seunggihong/ml-sklearn","last_synced_at":"2025-07-08T07:11:39.818Z","repository":{"id":208544387,"uuid":"721886439","full_name":"seunggihong/ML-Sklearn","owner":"seunggihong","description":" Simple machine learning model using scikit-learn","archived":false,"fork":false,"pushed_at":"2023-12-14T05:46:09.000Z","size":72,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":3,"default_branch":"main","last_synced_at":"2024-01-29T00:05:09.623Z","etag":null,"topics":["adaboost","bagging","classfication","decision-tree","discriminant-analysis","gaussian-naive-bayes","gridsearchcv","knn","machine-learning","multinomial-naive-bayes","random-forest","regression","scikit-learn","svm","voting"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/seunggihong.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2023-11-22T01:26:32.000Z","updated_at":"2023-11-28T12:28:28.000Z","dependencies_parsed_at":"2024-01-07T06:06:11.803Z","dependency_job_id":"3416077b-4f67-46d0-a817-c1ebb4b9e69b","html_url":"https://github.com/seunggihong/ML-Sklearn","commit_stats":{"total_commits":30,"total_committers":2,"mean_commits":15.0,"dds":"0.033333333333333326","last_synced_commit":"14b8e802a823686424d0a567b1279fd1902915a2"},"previous_names":["seunggihong/ml-sklearn"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/seunggihong%2FML-Sklearn","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/seunggihong%2FML-Sklearn/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/seunggihong%2FML-Sklearn/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/seunggihong%2FML-Sklearn/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/seunggihong","download_url":"https://codeload.github.com/seunggihong/ML-Sklearn/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247416677,"owners_count":20935512,"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","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":["adaboost","bagging","classfication","decision-tree","discriminant-analysis","gaussian-naive-bayes","gridsearchcv","knn","machine-learning","multinomial-naive-bayes","random-forest","regression","scikit-learn","svm","voting"],"created_at":"2025-04-06T00:18:58.915Z","updated_at":"2025-04-06T00:18:59.606Z","avatar_url":"https://github.com/seunggihong.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"![Static Badge](https://img.shields.io/badge/python3-3.11.5-%233776AB?style=plastic\u0026logo=python\u0026logoColor=white)\n![Static Badge](https://img.shields.io/badge/sklearn-1.3.2-%23F7931E?style=plastic\u0026logo=scikitlearn\u0026logoColor=white)\n![Static Badge](https://img.shields.io/badge/pandas-2.1.2-%23150458?style=plastic\u0026logo=scikitlearn\u0026logoColor=white)\n\n## ML-Sklearn\n\nThis repository uses scikit-learn to implement regression and classification models for machine learning algorithms. Then, evaluate each model and save and compare evaluation metrics. The data used in the regression analysis uses kaggle's `'red wine quilty'`, and the data used in the classification problem uses kaggle's `'Heart Failure Prediction'`. Additionally, I created a code showing how to find optimal hyperparameters using 'GridSearchCV'.\n\n\u003chr\u003e\n\n## Data\n\n![Static Badge](https://img.shields.io/badge/kaggle-Red%20Wine%20Quality-%2320BEFF?style=social\u0026logoColor=white\u0026labelColor=%2320BEFF)\n\n![Static Badge](https://img.shields.io/badge/kaggle-Heart%20Failure%20Prediction-%2320BEFF?style=social\u0026logoColor=white\u0026labelColor=%2320BEFF)\n\n- Regression\n\n  - [Red Wine Quality](https://www.kaggle.com/datasets/uciml/red-wine-quality-cortez-et-al-2009)\n\n- Classification\n\n  - [Heart Failure Prediction](https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction)\n\n\u003chr\u003e\n\n## Algorithm\n\n- [D-Tree](#dtree)\n- [RF](#rf)\n- [NB](#nb)\n  - Gaussian Naive Bayes\n  - Multinomial Naive Bayes\n- [K-NN](#knn)\n- [Ada](#ada)\n- [DA](#da)\n  - Linear Discriminant Analysis\n  - Quadratic Discriminant Analysis\n- [SVM](#svm)\n- [Voting](#voting)\n- [Bagging](#bagging)\n\n\u003chr\u003e\n\n\u003ca name='dtree'\u003e\u003c/a\u003e\n\n### D-Tree **_(Decision Tree)_**\n\n- **_Code_** [DTree.py](https://github.com/seunggihong/ML-Sklearn/blob/main/Algorithm/DTree.py)\n\n- **_Hyper parameters_**\n  ```json\n  \"dtree\": { \"max_depth\": [1, 2, 3, 4, 5], \"min_samples_split\": [2, 3] }\n  ```\n- **_Usage_**\n  ```bash\n  $ python3 main.py --prob={reg or class} --model=dtree\n  ```\n\n\u003chr\u003e\n\u003ca name='rf'\u003e\u003c/a\u003e\n\n### RF **_(Random Forest)_**\n\n- **_Code_** [RF.py](https://github.com/seunggihong/ML-Sklearn/blob/main/Algorithm/RF.py)\n\n- **_Hyper parameters_**\n  ```json\n  \"rf\": {\n        \"n_estimators\": [10, 100],\n        \"max_depth\": [6, 8, 10, 12],\n        \"min_samples_leaf\": [8, 12, 18],\n        \"min_samples_split\": [8, 16, 20]\n      }\n  ```\n- **_Usage_**\n  ```bash\n  $ python3 main.py --prob={reg or class} --model=rf\n  ```\n\n\u003chr\u003e\n\n\u003ca name='nb'\u003e\u003c/a\u003e\n\n### NB **_(Naive Bayes)_**\n\n- **_Code_** [NB.py](https://github.com/seunggihong/ML-Sklearn/blob/main/Algorithm/NB.py)\n\n**_Gaussian Naive Bayes(GNB)_**\n\n- **_Hyper parameters_**\n  ```json\n  \"gnb\": {\n        \"var_smoothing\": [1e-2, 1e-3, 1e-4, 1e-5, 1e-6]\n      }\n  ```\n- **_Usage_**\n  ```bash\n  $ python3 main.py --prob=class --model=gnb\n  ```\n\n**_Multinomial Naive Bayes(MNB)_**\n\n- **_Hyper parameters_**\n  ```json\n  \"mnb\": {\n        \"var_smoothing\": [1e-2, 1e-3, 1e-4, 1e-5, 1e-6]\n      }\n  ```\n- **_Usage_**\n  ```bash\n  $ python3 main.py --prob=class --model=mnb\n  ```\n\n\u003chr\u003e\n\n\u003ca name='knn'\u003e\u003c/a\u003e\n\n### K-NN **_(K Nearest Neighbors)_**\n\n- **_Code_** [KNN.py](https://github.com/seunggihong/ML-Sklearn/blob/main/Algorithm/KNN.py)\n\n- **_Hyper parameters_**\n  ```json\n  \"knn\": {\n        \"n_neighbors\": [1, 2, 3, 4, 5],\n        \"weights\": [\"uniform\", \"distance\"]\n      }\n  ```\n- **_Usage_**\n  ```bash\n  $ python3 main.py --prob={reg or class} --model=knn\n  ```\n\n\u003chr\u003e\n\n\u003ca name='ada'\u003e\u003c/a\u003e\n\n### Ada **_(Adaptive Boosting)_**\n\n- **_Code_** [Ada.py](https://github.com/seunggihong/ML-Sklearn/blob/main/Algorithm/Ada.py)\n\n- **_Hyper parameters_**\n  ```json\n  \"ada\": {\n        \"n_estimators\": [50, 100, 150],\n        \"learning_rate\": [0.01, 0.1]\n      }\n  ```\n- **_Usage_**\n  ```bash\n  $ python3 main.py --prob={reg or class} --model=ada\n  ```\n\n\u003chr\u003e\n\n\u003ca name='da'\u003e\u003c/a\u003e\n\n### DA **_(Discriminant Analysis)_**\n\n- **_Code_** [DA.py](https://github.com/seunggihong/ML-Sklearn/blob/main/Algorithm/DA.py)\n\n**_Linear Discriminant Analysis(LDA)_**\n\n- **_Hyper parameters_**\n  ```json\n  \"lda\": {\n        \"n_components\": [6, 8, 10, 12],\n        \"learning_decay\": [0.75, 0.8, 0.85]\n      }\n  ```\n- **_Usage_**\n  ```bash\n  $ python3 main.py --prob=class --model=lda\n  ```\n\n**_Quadratic Discriminant Analysis(QDA)_**\n\n- **_Hyper parameters_**\n  ```json\n  \"qda\": {\n        \"reg_param\": [0.1, 0.2, 0.3, 0.4, 0.5]\n      }\n  ```\n- **_Usage_**\n  ```bash\n  $ python3 main.py --prob=class --model=qda\n  ```\n\n\u003chr\u003e\n\n\u003ca name='svm'\u003e\u003c/a\u003e\n\n### SVM **_(Support Vector Machine)_**\n\n- **_Code_** [SVM.py](https://github.com/seunggihong/ML-Sklearn/blob/main/Algorithm/SVM.py)\n\n- **_Hyper parameters_**\n  ```json\n  \"svm\": {\n        \"C\": [0.1, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4],\n        \"kernel\": [\"linear\", \"rbf\"],\n        \"gamma\": [0.1, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4]\n      }\n  ```\n- **_Usage_**\n  ```bash\n  $ python3 main.py --prob={reg or class} --model=svm\n  ```\n\n\u003chr\u003e\n\n\u003ca name='voting'\u003e\u003c/a\u003e\n\n### Voting\n\n- **_Code_** [Voting.py](https://github.com/seunggihong/ML-Sklearn/blob/main/Algorithm/Voting.py)\n\n- **_Hyper parameters_**\n  ```json\n  Not yet\n  ```\n- **_Usage_**\n  ```bash\n  $ python3 main.py --prob={reg or class} --model=voting\n  ```\n\n\u003chr\u003e\n\n\u003ca name='bagging'\u003e\u003c/a\u003e\n\n### Bagging\n\n- **_Code_** [Bagging.py](https://github.com/seunggihong/ML-Sklearn/blob/main/Algorithm/Bagging.py)\n\n- **_Hyper parameters_**\n  ```json\n  Not yet\n  ```\n- **_Usage_**\n  ```bash\n  $ python3 main.py --prob={reg or class} --model=bagging\n  ```\n\n\u003chr\u003e\n\n## Reference\n\n- https://scikit-learn.org/stable/user_guide.html\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fseunggihong%2Fml-sklearn","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fseunggihong%2Fml-sklearn","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fseunggihong%2Fml-sklearn/lists"}