/FicaUAI

FicaUAI - Fica University Artificial Intelligence 📈 - ANN MLP Data Science - Dropout - This application provides a graphical user interface (GUI) for managing and analyzing student data to predict dropout rates using an Artificial Neural Network (ANN) with a Multilayer Perceptron (MLP). Brasilian Portuguese: Evasão Universitária.

Primary LanguagePython

DevFel

📊 FicaUAI - Fica University Artificial Intelligence 📈

ANN MLP Data Science - Evasão - This application provides a graphical user interface (GUI) for managing and analyzing student data to predict dropout rates using an Artificial Neural Network (ANN) with a Multilayer Perceptron (MLP).

The interactive interface and all the tabs that you can use in the system.

FicaUAI Iterface Gif

🌟 Features

  • Load and validate structured student data from Excel files.
  • Configure model parameters and select covariates for dropout predictions.
  • Display detailed results and metrics including confusion matrices and accuracy.
  • Save and export enriched data with dropout probabilities back to Excel.
  • Change real student data and create new hypothecal scenarios to predict dropout rates.
  • List all non-dropout students and their probabilities to identify potential risks.

⚙️ Installation and Setup

  1. Clone the Repository: Begin by cloning the repository to your local machine:

    git clone https://github.com/devfel/FicaUAI.git

  2. Navigate to the Directory:

    cd FicaUAI

  3. Install the Required Libraries: Ensure you have the required libraries installed:

    pip install numpy pandas scikit-learn pillow tk_tools tkinter

🚀 Getting Started

  1. Execute gui.py to open the graphical interface. "py gui.py" or "python gui.py"
  2. Follow the GUI prompts to load data, configure the model, and view results.

📖 Usage Examples

  • Load Data: Load your student data through the GUI and ensure it meets the format requirements.
  • Run Predictions: Configure the model parameters and run the prediction model.
  • Save and Export: Save the results and predictions back into an Excel file with the added probabilities.

🔥 Execution

To run the program, navigate to the project's main directory and execute:

python gui.py

🔧 Requirements

  • Python 3.x
  • Libraries: numpy, pandas, scikit-learn, tkinter, tk_tools, pillow
  • Excel files for data input/output

📂 Directory Structure

  • input/: Directory to place your input Excel files.
  • output/: Directory where the enriched Excel files will be saved.
  • gui.py: Main script to launch the GUI.
  • main.py: Backend script handling data operations and model training.

🙌 Contribution

Feel free to fork the project, open issues, and provide pull requests.

📜 License

This project is licensed under the MIT License.