https://github.com/abrahamkoloboe27/regression-application-streamlit
Lien de l'application
https://github.com/abrahamkoloboe27/regression-application-streamlit
data-processing fine-tuning machine-learning machine-learning-algorithms pycaret python regression-models streamlit training-model visualization
Last synced: 10 months ago
JSON representation
Lien de l'application
- Host: GitHub
- URL: https://github.com/abrahamkoloboe27/regression-application-streamlit
- Owner: abrahamkoloboe27
- License: mit
- Created: 2024-07-04T17:08:58.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-05T15:14:31.000Z (almost 2 years ago)
- Last Synced: 2025-03-27T01:35:31.830Z (about 1 year ago)
- Topics: data-processing, fine-tuning, machine-learning, machine-learning-algorithms, pycaret, python, regression-models, streamlit, training-model, visualization
- Language: Python
- Homepage: https://regression-application-app-zach27.streamlit.app/
- Size: 1.18 MB
- Stars: 2
- Watchers: 1
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Pages Overview](#pages-overview)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#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:**
```sh
git clone https://github.com/abrahamkoloboe27/Regression-Application-Streamlit.git
```
2. **Navigate to the project directory:**
```sh
cd Regression-Application-Streamlit
```
3. **Install the required packages:**
```sh
pip install -r requirements.txt
```
## 🚀 Usage
To start the application, run the following command:
```sh
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](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](https://www.linkedin.com/in/abraham-zacharie-koloboe-data-science-ia-generative-llms-machine-learning/)
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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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