Welcome to the Churn Prediction Application repository. This Flask-based web application is designed to predict customer churn using a machine learning model trained on customer data.
The Churn Prediction Application assists businesses in understanding and predicting customer behavior, especially the likelihood of customers discontinuing their use of a service. The application processes customer data through a web form and provides a churn prediction along with the associated probability.
- Churn Prediction Form: A user-friendly web form for inputting customer data.
- Prediction Results: Displays the churn prediction and probability after processing the input data.
- Machine Learning Model: Leverages a pre-trained model to make predictions based on input data.
To set up the application locally:
- Clone the repository:
git clone https://github.com/SteveUseful/churnmaster.git
- Navigate to the project directory:
cd churnmaster
- Install the required dependencies (Python and pip should be installed):
pip install -r requirements.txt
- Run the Flask application:
python app.py
Once the application runs, access it by navigating to http://127.0.0.1:5000
in your web browser. Fill out the churn prediction form with relevant customer data and submit it to receive a prediction.
app.py
: Main Flask application file with routes and prediction logic.best_model2.pkl
: Pre-trained machine learning model for predictions.preprocessor.pkl
: Preprocessing pipeline for input data.templates/
: HTML templates for the web application.index2.html
: HTML template for the churn prediction form.static/
: Static files such as CSS and JavaScript (if applicable).
Contributions to improve the application are welcome. Please follow the standard GitHub pull request process to submit your changes.
This project is licensed under the MIT License - see the LICENSE file for details.
For any queries or further assistance, please contact me on GitHub!
Thanks to all the contributors who have helped develop this application, and special thanks to the open-source community for providing the tools and libraries used in this project.
!GitHub stats
Python 7 hrs 30 mins ██████████████████░░░░░░░ 72.00 %
SQL 1 hr 30 mins ███░░░░░░░░░░░░░░░░░░░░░░ 14.50 %
Other 1 hr ███░░░░░░░░░░░░░░░░░░░░░░ 9.50 %
Feel free to reach out if you have any questions or need assistance with your projects!