This project demonstrates the implementation of Support Vector Machines (SVMs) from scratch using Python and compares their performance with SVMs implemented using the Scikit-learn library. The project includes three different kernel functions: Linear, Polynomial, and Radial Basis Function (RBF).
Support Vector Machines are powerful machine learning models used for classification and regression tasks. In this project, we implement SVMs from scratch with three different kernel functions and compare their performance with Scikit-learn's SVM implementation. The dataset used for this project is the 'heart' dataset, where the goal is to predict the presence of heart disease.
The project is structured as follows:
svm_scratch.py: Contains the implementation of SVM class with different kernel functions, cross-validation split function, and hyperparameter tuning.heart.csv: Dataset containing heart-related features and target labels.README.md: This README file providing an overview of the project.
- Clone this repository to your local machine.
- Ensure you have the required dependencies (see Dependencies).
- Run the
svm_scratch.pyscript to train and test SVMs using different kernel functions. - The script will output classification reports for each SVM, including precision, recall, F1-score, and accuracy metrics.
After running the svm_scratch.py script, you will see the results of training and testing SVMs with different kernel functions. The classification reports will provide insights into the performance of each SVM, including precision, recall, F1-score, and accuracy metrics.
The project relies on the following libraries:
numpy: For numerical operations.pandas: For data manipulation and analysis.sklearn: For Scikit-learn's SVM implementation and classification report generation.
Ensure you have these libraries installed before running the script.
This project is licensed under the MIT License.