Update README.md


README.md

@@ -10,4 +10,76 @@ metrics:
 library_name: keras
 tags:
 - finance
----
+---
+# 💰 Bank Churn Prediction — AI for Smarter Customer Retention  
+
+## 🧩 Overview  
+Businesses like banks which provide service have to worry about the problem of *Customer Churn* — i.e. customers leaving and joining another service provider.  
+It is important to understand which aspects of the service influence a customer's decision in this regard.  
+Management can concentrate efforts on improvement of service, keeping in mind these priorities.  
+
+**Objective**  
+You as a Data Scientist with the bank need to build a neural network–based classifier that can determine whether a customer will leave the bank or not in the next 6 months.
+
+---
+
+## 🤖 Model Details  
+- **Model Type:** Feed-forward ANN (Artificial Neural Network) — Binary Classifier  
+- **Framework:** TensorFlow / Keras  
+- **Dataset:** `Churn.csv` *(Bank Customer Churn Dataset, 10,000+ customers)*  
+- **Input:** Structured customer profile (credit score, age, balance, tenure, activity, salary, etc.)  
+- **Output:** Binary churn prediction (`0 = stays`, `1 = churn`)  
+
+---
+
+## 📊 Data Dictionary  
+| Feature             | Description                                                                |
+|---------------------|----------------------------------------------------------------------------|
+| `CustomerId`        | Unique ID assigned to each customer                                        |
+| `Surname`           | Customer’s last name                                                       |
+| `CreditScore`       | Customer credit history                                                    |
+| `Geography`         | Customer location                                                          |
+| `Gender`            | Gender of the customer                                                     |
+| `Age`               | Age of the customer                                                        |
+| `Tenure`            | Number of years with the bank                                              |
+| `NumOfProducts`     | Number of bank products purchased                                          |
+| `Balance`           | Account balance                                                            |
+| `HasCrCard`         | Whether the customer has a credit card                                     |
+| `EstimatedSalary`   | Estimated salary                                                           |
+| `IsActiveMember`    | Whether the customer is an active member                                   |
+| `Exited`            | Target label — 0: No (customer stays), 1: Yes (customer churns)            |
+
+---
+
+## 🌟 Why It Matters  
+✅ Detects early signs of potential churn  
+✅ Enables targeted retention strategies  
+✅ Improves customer engagement and loyalty  
+✅ Helps maximize profitability and reduce attrition rates  
+
+📘 **Full Source Notebook:**  
+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  
+```python
+from tensorflow.keras.models import load_model
+import numpy as np
+
+# Load trained model
+model = load_model("bank_churn_model.h5")
+
+# 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]])
+
+# Get churn probability
+pred = model.predict(sample)
+print("Churn Probability:", pred[0][0])
+
+if pred[0][0] > 0.5:
+    print("Prediction: Customer likely to churn")
+else:
+    print("Prediction: Customer likely to stay")