# Bank Customer Churn Prediction Model Card

## Model Details

- **Architecture:** Artificial Neural Network (ANN) with 2 hidden layers (12 and 6 units, ReLU activation), output layer (1 unit, sigmoid activation).
- **Framework:** Keras (TensorFlow backend)
- **Input Features:** 14 normalized and engineered features:
    - CreditScore, Gender, Age, Tenure, Balance, NumOfProducts, HasCrCard, IsActiveMember, EstimatedSalary, BalanceSalaryRatio, TenureByAge, Geography_France, Geography_Germany, Geography_Spain
- **Output:** Binary classification (Exited: 0 = retained, 1 = churned)

## Intended Use

- Predict whether a bank customer will churn (exit) based on their profile and account activity.
- Useful for financial institutions to identify at-risk customers and take retention actions.

## Training Data

- **Dataset:** Custom bank churn dataset (`churn.csv`)
- **Size:** 10,000 samples
- **Split:** 80% train, 20% test
- **Preprocessing:** Feature engineering (BalanceSalaryRatio, TenureByAge), categorical encoding, min-max scaling.

## Metrics

- **Loss:** Binary cross-entropy
- **Accuracy:** ~81% on test set
- **Evaluation:** Confusion matrix, classification report

## Limitations

- Model trained on a specific dataset; may not generalize to other banks or regions.
- Sensitive to feature distribution and preprocessing steps.
- Does not explain feature importance.