Create README.md

README.md

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+# 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.