This repository contains the code for a Human Machine Interface (IHM) developed as part of a project for end-of-studies. The IHM is designed to interact with Support Vector Machine (SVM) machine learning models. This README provides an overview of the project, installation instructions, and usage guidelines.

• User-friendly interface for interacting with SVM models.
• Load pre-trained SVM models.
• Visualize data.
• Perform model evaluation.

• Python 3.x
• Required Python libraries: tkinter, scikit-learn, numpy, matplotlib

1. Clone this repository to your local machine:

git clone https://github.com/zineb1224/PFEProject.git

2. Navigate to the project directory:

cd PFEProject

3. Install the required Python libraries:

pip install -r requirements.txt

1. Run the main application script:

python interfaceGraphique.py

2. Use the interface to load or train SVM models, visualize data, make predictions, and evaluate model performance.

Contributions are welcome! If you find any bugs or have suggestions for improvements, please open an issue or submit a pull request.

This project is licensed under the MIT License.

• The development of this project was inspired by the need for a user-friendly interface for SVM machine learning models.
• Special thanks to [contributors] for their valuable input and feedback.

Feel free to customize this template according to your project's specifics.