Classification Model Evaluation Project

This project provides a framework for evaluating classification models using common metrics such as accuracy, precision, recall, and F1 score. It includes a simple implementation of a Support Vector Machine (SVM) classifier and utilities for evaluating its performance.

Table of Contents

1. Project Overview
2. Setup
3. Running the Project
4. Features
5. Usage
6. Contributing
7. License
8. Acknowledgments

Project Overview

This project aims to demonstrate best practices in evaluating classification models. It includes:

• A simple SVM implementation for binary classification
• Utility functions for calculating common evaluation metrics
• A main script to load data, train the model, and perform evaluations
• Unit tests for the implemented functions

The project uses scikit-learn for the SVM implementation and numpy for numerical operations. It also includes matplotlib for visualization of results.

Setup

1. Clone the repository:

   git clone https://github.com/yourusername/classification-model-evaluation.git
   

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
   

3. Install dependencies:

   pip install -r requirements.txt
   

Running the Project

To run the project:

1. Navigate to the project directory:

   cd classification-model-evaluation
   

2. Activate the virtual environment (if not already activated):

   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
   

3. Run the main script:

   python src/main.py
   

This will load sample data, train the SVM model, and display evaluation metrics.

Features

• Simple SVM Implementation: Uses scikit-learn's SVC for binary classification.
• Evaluation Metrics: Calculates accuracy, precision, recall, and F1 score.
• Visualization Tools: Prints classification report and confusion matrix.
• Unit Tests: Includes tests for the implemented functions.

Usage

The project can be easily extended or modified:

1. To change the classifier, modify the src/models/model.py file.
2. To add new evaluation metrics, edit the src/utils/evaluation_metrics.py file.
3. To use different datasets, replace the data loading logic in src/main.py.

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a new branch (git checkout -b feature/AmazingFeature)
3. Make your changes
4. Commit your changes (git commit -m 'Add some amazing feature')
5. Push to the branch (git push origin feature/AmazingFeature)
6. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Acknowledgments

• This project was inspired by various machine learning tutorials and best practices.
• Special thanks to the scikit-learn team for providing excellent libraries and documentation.
• Thanks to contributors who have helped shape this project into its current form.