Update README.md

README.md

@@ -61,24 +61,71 @@ The complete training and evaluation notebook is available on GitHub:
 👉 [View on GitHub](https://github.com/joyjitroy/Machine_Learning/blob/main/Bank_Customer_Churn_Prediction_using_Artificial_Neural_Networks.ipynb)
 
 ---
+## 🚀 Example Usage
 
-## 🚀 Example Usage  
 ```python
-from tensorflow.keras.models import load_model
+# Inference example aligned to your dataset schema
+
+import pandas as pd
 import numpy as np
 
-# Load trained model
-model = load_model("bank_churn_model.h5")
+CSV_PATH = "Data/Churn.csv"
+TARGET = "Exited"
+
+# Columns exactly as in your table
+ALL_COLS = [
+    "CustomerId","Surname","CreditScore","Geography","Gender","Age","Tenure",
+    "NumOfProducts","Balance","HasCrCard","EstimatedSalary","IsActiveMember","Exited"
+]
+
+NUM_COLS = ["CreditScore","Age","Tenure","NumOfProducts","Balance","EstimatedSalary","HasCrCard","IsActiveMember"]
+CAT_COLS = ["Geography","Gender"]
+DROP_COLS = ["CustomerId","Surname"]
+
+# Load data
+df = pd.read_csv(CSV_PATH)[ALL_COLS]
+
+def prepare_features(df_in, fit_cols=None):
+    X = df_in.drop(columns=[TARGET] + DROP_COLS).copy()
+    # one hot on the two categoricals
+    X = pd.get_dummies(X, columns=CAT_COLS, drop_first=True)
+    # align to training columns
+    if fit_cols is not None:
+        X = X.reindex(columns=fit_cols, fill_value=0)
+    return X
+
+# If you trained in this notebook, reuse `model` and `feature_cols` from training:
+#   model.save("bank_churn_ann.keras")
+#   feature_cols = X_train.columns.tolist()
+
+# If loading a saved model:
+# from tensorflow.keras.models import load_model
+# model = load_model("bank_churn_ann.keras")
+# feature_cols = [...]  # same list you used during training after get_dummies
+
+# Single example constructed with your schema
+example = {
+    "CustomerId": 15788241,
+    "Surname": "Smith",
+    "CreditScore": 600,
+    "Geography": "Germany",   # France, Germany, Spain in this dataset
+    "Gender": "Male",         # Male or Female
+    "Age": 40,
+    "Tenure": 3,
+    "NumOfProducts": 2,
+    "Balance": 60000.0,
+    "HasCrCard": 1,
+    "EstimatedSalary": 50000.0,
+    "IsActiveMember": 1,
+    "Exited": 0               # ignored at inference
+}
+
+ex_df = pd.DataFrame([example])
+X_ex = prepare_features(ex_df, fit_cols=feature_cols)
 
-# Example customer record (normalized)
-# [CreditScore, Age, Tenure, Balance, NumOfProducts, HasCrCard, IsActiveMember, EstimatedSalary, Geography_Germany, Geography_Spain, Gender_Male]
-sample = np.array([[600, 40, 3, 60000, 2, 1, 1, 50000, 0, 1, 1]])
+pred_prob = float(model.predict(X_ex, verbose=0)[0][0])
+pred = int(pred_prob >= 0.5)
 
-# Get churn probability
-pred = model.predict(sample)
-print("Churn Probability:", pred[0][0])
+print(f"Churn Probability: {pred_prob:.4f}")
+print("Prediction:", "Likely to churn" if pred else "Likely to stay")
 
-if pred[0][0] > 0.5:
-    print("Prediction: Customer likely to churn")
-else:
-    print("Prediction: Customer likely to stay")