yasser942 / dss_project

Decision Support System (DSS) Project

Overview

The Decision Support System (DSS) project is a machine learning application built using C# and Weka, aimed at assisting decision-making processes through automated analysis of datasets. This project provides a user-friendly interface for users to upload datasets, evaluate multiple machine learning algorithms, and make predictions based on the selected model.

Key Features

• Algorithm Evaluation: Evaluate various machine learning algorithms, including Naive Bayes, K Nearest Neighbor, Decision Trees, Neural Networks, and Support Vector Machines, to determine the most suitable model for a given dataset.
• Data Preprocessing: Support for data preprocessing techniques such as normalization and nominal to binary conversion to enhance the quality of analysis.
• Cross-Validation: Perform cross-validation to assess the accuracy and reliability of each algorithm on the provided dataset.
• Real-Time Feedback: Provide real-time feedback on algorithm performance and success rates to guide decision-making.

Getting Started

1. Clone the Repository: Clone this repository to your local machine using https://github.com/yasser942/DSS_Project.git.
2. Install Dependencies: Ensure you have Weka installed on your system. You can download it from Weka's official website.
3. Open Solution: Open the DSS_LastAssignment.sln solution file in Visual Studio.
4. Build and Run: Build the solution and run the MainForm.cs file to launch the application.

Usage

1. Upload Dataset: Click on the "Browse" button to select and upload a dataset in ARFF format.
2. Evaluate Algorithms: Click on the "Evaluate" button to evaluate multiple machine learning algorithms on the uploaded dataset.
3. View Results: The application will display the best-performing algorithm along with its success rate.
4. Make Predictions: Use the "Discover" button to input new data instances for prediction using the selected algorithm.

Contributing

Contributions are welcome! If you have any suggestions, enhancements, or bug fixes, feel free to open an issue or create a pull request. For major changes, please open an issue first to discuss the proposed changes.

Acknowledgements

• Weka: The project utilizes Weka, a powerful machine learning library.
• Windows Forms: The user interface is built using Windows Forms for a seamless user experience.

Contact

For any inquiries or feedback, please contact [[email protected]].