🧠 ANN Customer Churn Classification 📖 Overview
This project implements an Artificial Neural Network (ANN) to predict customer churn for a subscription-based business. The model is trained on customer demographic and service usage data to classify whether a customer is likely to leave (churn) or stay (retain).
The project demonstrates how to preprocess categorical and numerical data, train a deep learning model, and deploy it using Streamlit for interactive predictions.
🛠️ Tech Stack 🔹 Programming & Libraries
Python
TensorFlow / Keras
NumPy, Pandas
Scikit-learn
🔹 Visualization
Matplotlib, Seaborn
🔹 Deployment
Streamlit
🔹 Model Files
ANN model saved as .h5
Encoders & scalers saved as .pkl
📂 Folder Structure ANN-Customer-Churn-Classification/ │── .devcontainer/ # Development container settings │── .streamlit/ # Streamlit configuration files │── Churn_Modelling.csv # Dataset │── LICENSE # Project license │── README.md # Project documentation │── app.py # Streamlit app for deployment │── experiments.ipynb # Notebook for model experimentation │── prediction.ipynb # Notebook for predictions │── model.h5 # Trained ANN model │── label_encoder_gender.pkl # Encoder for gender feature │── onehot_encoder_geo.pkl # Encoder for geography feature │── scaler.pkl # Feature scaler │── requirements.txt # Dependencies
🚀 Features
✅ Predicts whether a customer will churn or stay. ✅ Preprocessing of categorical (gender, geography) and numerical features. ✅ ANN built with multiple dense layers. ✅ Model deployed using Streamlit with a user-friendly interface. ✅ Encoders and scalers stored for consistent preprocessing.
Clone the repository
git clone https://github.com/prashanth316/ANN-Customer-Churn-Classification.git cd ANN-Customer-Churn-Classification
Install dependencies
pip install -r requirements.txt
Run the Streamlit app
streamlit run app.py
Open in browser:
📊 Example Output
Input: Customer details (age, geography, gender, balance, credit score, etc.)
Output:
Churn Prediction: Yes / No
Probability Score: e.g., 78% chance of churn
🔮 Future Enhancements
Add more customer features for improved accuracy.
Implement XAI (Explainable AI) to explain predictions.
Deploy the model as a REST API for integration with other systems.
👤 Author
Developed by Prashanth Reddy Kondapuram