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ML Algorithms with GUI

This Tkinter-based app allows users to upload CSV files, preprocess data, and apply machine learning algorithms like Random Forest, SVM, Decision Tree, Neural Network, KNN for classification, Linear Regression for regression, and K-Means for clustering. It displays metrics and visualizes results, offering an interface for machine learning tasks

Features

  • Implemented Algorithms:
    • Supervised Learning: Linear Regression, Logistic Regression, Decision Trees, Random Forest, etc.
    • Unsupervised Learning: K-Means Clustering, Hierarchical Clustering, PCA, etc.
    • Deep Learning (Optional): Integration with frameworks like TensorFlow or PyTorch.
  • Interactive GUI:
    • Built using libraries such as Tkinter, PyQt, or others.
    • Easy input of dataset, parameter tuning, and visualization of results.
  • Visualization:
    • Plots for training/validation performance.
    • Decision boundaries for classifiers.
    • Data distribution and clustering visuals.

Installation

  1. Clone the repository:

    git clone https://github.com/NASO7Y/ML_algorithms_with_GUI.git
cd ML_algorithms_with_GUI

  2. Install the required dependencies:

    pip install -r requirements.txt

Usage

  1. Run the GUI application:

    python main.py

  2. Load a dataset (CSV format recommended).

  3. Select the desired ML algorithm from the GUI menu.

  4. Adjust hyperparameters and visualize results.

File Structure

  • /algorithms: Contains implementations of various ML algorithms.
  • /gui: Includes GUI code and layout files.
  • /data: Sample datasets for testing.
  • requirements.txt: Python dependencies for the project.
  • main.py: Entry point for the application.

Dependencies

  • Python 3.x
  • Required libraries (see requirements.txt):
    • numpy
    • pandas
    • matplotlib
    • scikit-learn
    • GUI-specific library (tkinter/pyqt5)

Contributing

Contributions are welcome! Please follow these steps:

  1. Fork the repository.
  2. Create a new branch for your feature/bugfix.
  3. Commit your changes and submit a pull request.

Contact

GitHub: naso7y

Email: ahmed.noshy2004@gmail.com

LinkedIn: LinkedIn