Neural Network Studio App

Build and train artificial neural networks with an intuitive Streamlit GUI. Easily upload and explore your data, fine-tune model parameters, and visualize both your data and model performance—all in one streamlined app.

👉 Launch the app

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🚀 Features

• 📂 Data Upload: Easily upload your CSV dataset for analysis.
• 🧹 Data Cleaning & Preprocessing: (Coming soon) Handle missing values, remove duplicates, and prepare your data for modeling.
• 📊 Data Exploration & Visualization:
  • View summary statistics, missing values, and correlation matrices.
  • Generate interactive charts: area, box, histogram, scatter, heatmap, and pie.
• 🏗️ Build Artificial-Neural-Network:
  • Configure, train, and evaluate an ANN model on your data.
  • Adjust hyperparameters and monitor training progress.
  • Upload new (unseen) data to make predictions with your trained model.
  • Download prediction results as a CSV file.
• 🤖 About: Learn what each page does and how to use the app.

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Example: Visualizing a generated neural network with the app.

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🗂️ Project Organization

├── LICENSE
├── README.md
├── data
│   ├── external
│   ├── interim
│   ├── processed
│   └── raw
├── docs
├── models
├── notebooks
├── pyproject.toml
├── references
├── reports
│   └── figures
├── requirements.txt
└── src
    ├── __init__.py
    ├── about.py
    ├── config.py
    ├── data_exploration.py
    ├── data_preprocessing.py
    ├── dataset.py
    ├── features.py
    ├── main.py
    ├── model_build.py
    ├── visualization
    │   ├── __init__.py
    │   └── visualize.py
    └── modeling
        ├── __init__.py
        ├── predict.py
        └── train.py

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⚡ Getting Started

1. Install requirements:

   pip install -r requirements.txt
   

2. Run the app:

   streamlit run src/main.py
   

3. Open in your browser:
   Visit http://localhost:8501 (default Streamlit port).

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📄 License

See LICENSE for details.

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Author: Vahidin (Dean) Jupic
Version: 0.9.0