| # 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. |