Machine Learning Application for Classification and Regression (Advanced Machine Learning Course)

Description

This project applies machine learning algorithms to perform both classification and regression tasks. It focuses on three fundamental models: Support Vector Machines (SVM), Decision Trees, and Artificial Neural Networks (ANN). We have worked on two datasets, with a special emphasis on House Price Prediction for regression tasks, achieving high accuracies above 85%.

Introduction

The motivation behind this project is to explore the capabilities of machine learning in predicting outcomes with high accuracy. Classification and regression tasks form the core of many machine learning applications, providing insights and predictions based on data.

Features

• Implementation of SVM, Decision Trees, and ANN.
• Regression Analysis on the House Price Prediction dataset.
• Data Preprocessing to enhance model performance.
• High Accuracy Rates achieved (above 85%).
• GUI Interface using Tkinter for easy model predictions.

Installation Instructions

To set up the project environment, please follow next:

Clone the repository: git clone https://github.com/ABDELHALIM9/Advanced-Machine-Learning-

Usage

To run the machine learning models and use the GUI interface:

1. Navigate to the project directory.
2. make sure you have 2 environment to make

• the classification run on pytorch Version: 2.15.0
• the regression run on pytorch Version: 2.16.1
• Finally Make sure that you install other libiraries on another versions

3. Run the command: python GUI\GUI_main.py to show the visualization

File Structure

• GUI/: Directory containing the Tkinter GUI interface files.
• Classification/: Directory containing the jupyter notebook with model training and save models for GUI.
• Heart_Disease_Cls.ipynb: Jupyter Notebook with model training code.
• Regression/: Directory containing the jupyter notebook with model training and save models for GUI.
• ad-ml-house-price.ipynb: Jupyter Notebook with model training code.

Results

The models trained on the House Price Prediction dataset achieved accuracies above 85%. For detailed results, please refer to the results section in the Jupyter Notebook.

Contributing

We welcome contributions! If you would like to contribute, please:

• Report any bugs or issues.
• Submit feature requests.
• Propose improvements or enhancements.

Contact Information

For any queries or feedback, please contact us at email-email@example.com.

Acknowledgements

Special thanks to everyone who contributed to the development of this project.

References

For more information on the machine learning algorithms used, please refer to the following resources:

• Support Vector Machines
• Decision Trees
• Artificial Neural Networks

Used Tools & Libraries:

• VS code
• Kaggle
• Python
• Matplotlib library
• NumPy library
• Seaborn library
• scikit-Learn library
• tensorflow
• xgboost