{"id":26107057,"url":"https://github.com/andrepradika/ml-regression-models-compares","last_synced_at":"2026-04-17T20:31:36.213Z","repository":{"id":281488976,"uuid":"945435678","full_name":"andrepradika/ml-regression-models-compares","owner":"andrepradika","description":"Machine Learning Regression Models project! 🎉 This project is all about comparing different regression models to predict output variables based on input data. 📊","archived":false,"fork":false,"pushed_at":"2025-03-09T12:29:41.000Z","size":1127,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-09T13:26:55.862Z","etag":null,"topics":["data-science","machine-learning","model-comparison","python","regression-models"],"latest_commit_sha":null,"homepage":"","language":null,"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/andrepradika.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":"2025-03-09T12:21:55.000Z","updated_at":"2025-03-09T12:29:45.000Z","dependencies_parsed_at":"2025-03-09T14:30:32.323Z","dependency_job_id":null,"html_url":"https://github.com/andrepradika/ml-regression-models-compares","commit_stats":null,"previous_names":["andrepradika/ml-regression-models-compares"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/andrepradika/ml-regression-models-compares","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andrepradika%2Fml-regression-models-compares","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andrepradika%2Fml-regression-models-compares/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andrepradika%2Fml-regression-models-compares/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andrepradika%2Fml-regression-models-compares/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/andrepradika","download_url":"https://codeload.github.com/andrepradika/ml-regression-models-compares/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andrepradika%2Fml-regression-models-compares/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31944955,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-17T17:29:20.459Z","status":"ssl_error","status_checked_at":"2026-04-17T17:28:47.801Z","response_time":62,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 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":["data-science","machine-learning","model-comparison","python","regression-models"],"created_at":"2025-03-09T22:43:02.513Z","updated_at":"2026-04-17T20:31:36.173Z","avatar_url":"https://github.com/andrepradika.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Machine Learning Regression Models\n\nThis project compares multiple machine learning regression models to predict outputs based on input data. The models included in this project are:\n\n- Linear Regression\n- Polynomial Regression (Degree 2)\n- Ridge Regression\n- Lasso Regression\n- Decision Tree Regressor\n- Random Forest Regressor\n- Gradient Boosting Regressor\n- Support Vector Regression (SVR)\n- K-Nearest Neighbors (KNN)\n\n## Project Overview\n\nThe goal of this project is to evaluate and compare different regression models on a given dataset to predict an output variable (`y`) based on two input variables (`input1` and `input2`). The models are evaluated based on three performance metrics:\n\n- Mean Absolute Error (MAE)\n- Mean Squared Error (MSE)\n- R² Score\n\n## Requirements\n\nTo run this project, you'll need the following Python libraries:\n\n- pandas\n- numpy\n- scikit-learn\n\nYou can install them using the following command:\n\n```bash\npip install pandas numpy scikit-learn\n```\n\n## Dataset\n\nThe dataset used in this project is a `.xlsx` file (`data 1 1.xlsx`) containing two input columns (`input1`, `input2`) and one output column (`output`). The dataset is loaded and preprocessed to split into training and testing sets for model evaluation.\n\n### Example Dataset Format:\n\n| input1 | input2 | output |\n|--------|--------|--------|\n|   ...  |   ...  |   ...  |\n|   ...  |   ...  |   ...  |\n\n## Code Overview\n\n### Steps:\n\n1. **Load Dataset**: The dataset is loaded from an Excel file.\n2. **Data Splitting**: The data is split into training and testing sets using an 80-20 split ratio.\n3. **Model Training**: A dictionary of models is defined and trained on the training data.\n4. **Model Evaluation**: Each model is evaluated on the test data using MAE, MSE, and R² Score.\n5. **Results**: The results are collected in a DataFrame and displayed for comparison.\n\n### Code Execution:\n\nTo run the script, simply run the Python code, and the results will be printed as a comparison of each model's performance:\n\n```bash\npython main.py\n```\n\n### Example Output:\n\n```\n                        MAE       MSE   R2 Score\nLinear Regression        0.1234    0.0456   0.89\nPolynomial Regression    0.1123    0.0394   0.91\nRidge Regression         0.1189    0.0421   0.90\nLasso Regression         0.1245    0.0471   0.88\nDecision Tree            0.1452    0.0563   0.82\nRandom Forest            0.1156    0.0410   0.92\nGradient Boosting        0.1084    0.0372   0.93\nSVR                      0.1323    0.0504   0.87\nK-Nearest Neighbors      0.1376    0.0531   0.85\n```\n\n## Conclusion\n\nThis project demonstrates how to compare multiple regression models on a given dataset, helping to choose the best model for a specific task based on their performance metrics.\n\nFeel free to modify the dataset and experiment with different hyperparameters to further improve the results.\n\n## License\nMIT License\n\n## Author\nandrepradika\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fandrepradika%2Fml-regression-models-compares","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fandrepradika%2Fml-regression-models-compares","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fandrepradika%2Fml-regression-models-compares/lists"}