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Streamlit app to predict customer churn using Ridge Regression.

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Predict Customer Churn with Streamlit 🚀

This application uses Machine Learning to predict whether a customer will leave a service (churn). The app is developed with Python and Streamlit, providing an interactive and user-friendly interface to make predictions based on provided data.

User Interface

Usage

1. Enter Customer Features

Use the sidebar to input customer data.

2. Make a Prediction

Click the "Predict" button to get the result.

3. View Historical Results

Check the "Historical Predictions" section to analyze past predictions.

User Interface

Project Structure

app.py: Main code for the Streamlit application.

utils.py: Helper functions, including data transformation.

data/: Contains necessary data files such as schema.json and historical_data.csv.

models/: Contains pretrained models (xg.pkl and encoder.pkl).

requirements.txt: List of dependencies required to run the application.

Contributions

Contributions are welcome. If you have ideas to improve this application, please open an issue or create a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

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Streamlit app to predict customer churn using Ridge Regression.

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