{"id":25866125,"url":"https://github.com/nimeshrawanage/ml-model-comparison-r","last_synced_at":"2026-06-07T02:31:58.447Z","repository":{"id":278476146,"uuid":"935745265","full_name":"NimeshRawanage/ML-Model-Comparison-R","owner":"NimeshRawanage","description":"📊 ML Model Comparison | Predictive Analysis of the Wisconsin Dataset - Evaluating Random Forest, KNN, and SVM for breast cancer classification in R.  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The goal is to determine which model provides the highest prediction accuracy for breast cancer diagnosis.  \n\n-------------------------\n\n## Project Structure  \n\nML-Model-Comparison-R/  \n├── data/                # Contains the dataset (wisconsin.csv)  \n├── models/              # Stores trained model files (e.g., knn_model.RData)  \n├── results/             # Stores evaluation results and plots  \n├── src/                 # Source code folder  \n│   ├── preprocess.R     # Data Preprocessing  \n│   ├── train_knn.R      # KNN Model Training  \n│   ├── train_rf.R       # Random Forest Model Training  \n│   ├── train_svm.R      # SVM Model Training  \n│   ├── evaluate.R       # Model Evaluation and Metrics  \n│   ├── visualize.R      # Data Visualization  \n│   ├── main.R           # Master Script (Runs all steps)  \n├── README.md            # Project documentation  \n├── LICENSE              # Open-source license  \n  \n\n-------------------------\n\n## Dataset Information\n  \n- **Dataset Name:** Wisconsin Breast Cancer Dataset  \n- **Source:** [Kaggle Dataset](https://www.kaggle.com/datasets/uciml/breast-cancer-wisconsin-data)  \n- **License:** CC BY-NC-SA 4.0    \n\n-------------------------\n\n## Machine Learning Models \n \n- **Random Forest (RF):** Ensemble method using 100 decision trees.  \n- **K-Nearest Neighbours (KNN):** Implemented with `k=5` neighbors.  \n- **Support Vector Machines (SVM):** Default hyperparameters with a linear kernel.  \n\n-------------------------\n\n## How to Run This Project  \n\n### Prerequisites \n \n- R installed (version \u003e= 4.0)  \n- Required libraries: `ggplot2`, `caret`, `randomForest`, `class`, `e1071`, `dplyr`  \n\n### Clone and Set Up the Repository  \n\ngit clone https://github.com/NimeshRawanage/ML-Model-Comparison-R.git  \ncd ML-Model-Comparison-R  \n\n### Install Required R Packages\n  \ninstall.packages(c(\"ggplot2\", \"caret\", \"randomForest\", \"e1071\", \"class\", \"dplyr\"))  \n\n### Run the Project  \n\nsource(\"src/main.R\")  \n\n-------------------------\n\n## Future Improvements \n \n- Implement hyperparameter tuning for all models.  \n- Introduce additional machine learning algorithms for comparison.  \n- Develop a web-based interface using Shiny for interactive model testing.  \n\n-------------------------\n\n## License  \n\nThis project is licensed under the MIT License. See the LICENSE file for details.  \n\n-------------------------\n\n## Author  \n\n**Nimesh Rawanage**  \n- GitHub: https://github.com/NimeshRawanage  \n- LinkedIn: https://www.linkedin.com/in/nimeshrawanage/  \n\n-------------------------\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnimeshrawanage%2Fml-model-comparison-r","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnimeshrawanage%2Fml-model-comparison-r","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnimeshrawanage%2Fml-model-comparison-r/lists"}