Welcome to my Machine Learning Web App project! This project is built using logistic regression and the sklearn library for training and data splitting. The trained model is then deployed as a web application using the "Streamlit" library and hosted on Heroku.
In this project, I developed a simple yet effective machine learning model using logistic regression. The model is trained on a dataset and used to predict outcomes based on the input data. The web application built using Streamlit allows users to interact with the model and see its predictions in real-time.
Click here to see the live demo of the web application.
- Train a logistic regression model
- Interact with the model through a user-friendly web interface
- Real-time predictions on user input
- Easy-to-use and intuitive UI
- Python
- scikit-learn (sklearn)
- Streamlit
- Heroku
To set up the project locally, follow these steps:
- Clone the repository:
git clone https://github.com/your-username/your-repo.git - Navigate to the project directory:
cd your-repo - Install the required dependencies:
pip install -r requirements.txt
To run the web application locally, use the following command:
streamlit run app.pyOpen your web browser and go to http://localhost:8501 to access the application.
The web app is deployed on Heroku and can be accessed at your-app-url.com.
Contributions are welcome! If you find any bugs or want to add new features, please feel free to open an issue or submit a pull request.
This project is licensed under the MIT License. Feel free to use and modify the code as per the terms of the license.
Thank you for visiting my project! If you have any questions or suggestions, feel free to contact me at your-email@example.com. Happy coding! 🚀💻