Customer Churn Prediction App This is a Streamlit web application for predicting customer churn. The app uses a machine learning model trained on customer data to predict whether a customer is likely to churn or not. It includes a user interface where users can input information about a customer, and the app will provide a prediction based on the trained model.
Getting Started Prerequisites Make sure you have the following dependencies installed:
Python 3.x TensorFlow pandas scikit-learn joblib Streamlit Install the required packages using:
bash Copy code pip install tensorflow pandas scikit-learn joblib streamlit Usage Clone the repository: bash Copy code git clone https://github.com/your-username/customer-churn-prediction-app.git cd customer-churn-prediction-app Run the Streamlit app: bash Copy code streamlit run app.py Open the provided URL in your web browser to access the application. Input Features The user can input the following features for prediction:
Tenure Monthly Charges Total Charges Contract Payment Method Online Security Tech Support Internet Service Predictions Once the user inputs the required features and clicks on the "Predict" button, the app will display the model's prediction whether the customer will churn or not along with the confidence of the prediciton.
Model Information The machine learning model is built using TensorFlow and saved as a .h5 file (model.h5). The preprocessing steps, such as scaling and label encoding, are stored in the scaler.pkl file.
Acknowledgments The model was trained on the "Customer Churn" dataset. The application is created by Sedem Amediku.
Link to model test: https://github.com/sedemkofi/88512025_Churning_Customers/blob/1bc925d0d832f5630226600f2fd7e1d2debee656/Model%20Test.mp4