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Regression Application with Streamlit and PyCaret

Welcome to the Regression Application repository! This application provides an intuitive interface for performing end-to-end regression analysis using Streamlit and PyCaret. It guides the user through various steps including data import, preprocessing, model training, fine-tuning, visualization, and final model deployment.

📋 Table of Contents

✨ Features

  • 📥 Data Import: Upload your own dataset or use predefined datasets.
  • 🔧 Setup: Configure preprocessing steps and training parameters.
  • 🤖 Train Models: Train multiple regression models and compare their performance.
  • 🔨 Fine-Tuning: Optimize model performance with various fine-tuning techniques.
  • 📈 Regression Plots: Visualize model performance through interactive plots.
  • 🔮 Make Predictions: Use the trained models to make predictions.
  • 💾 Finalization and Saving: Save the best performing model for deployment.

⚙️ Installation

To run this application locally, follow these steps:

  1. Clone the repository:
    git clone https://github.com/abrahamkoloboe27/Regression-Application-Streamlit.git
  2. Navigate to the project directory:
    cd Regression-Application-Streamlit
  3. Install the required packages:
    pip install -r requirements.txt

🚀 Usage

To start the application, run the following command:

streamlit run app.py

Once the application is running, you can access it in your web browser at http://localhost:8501.

📄 Pages Overview

  1. Home Page 📥:

    • Import your dataset and select the target variable.
  2. Setup 🔧:

    • Configure data preprocessing and training settings.
  3. Train Models 🤖:

    • Train multiple regression models and compare their performance.
  4. Fine-Tuning 🔨:

    • Optimize the selected models using fine-tuning techniques.
  5. Regression Plots 📈:

    • Visualize and compare the performance of trained models through various plots.
  6. Make Predictions 🔮:

    • Use the trained models to make predictions on new data.
  7. Finalization and Saving 💾:

    • Finalize and save the best model for deployment.

🤝 Contributing

Contributions are welcome! If you have any ideas, suggestions, or bug reports, feel free to open an issue or submit a pull request.

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

📞 Contact

If you have any questions or feedback, feel free to reach out to me:


Thank you for using this regression application! If you find it useful, please consider giving the repository a star ⭐.


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