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
• Installation
• Usage
• Pages Overview
• Contributing
• License
• Contact

✨ 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:

• Email: abklb27@gmail.com
• LinkedIn: Abraham Z. KOLOBOE

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

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