---
license: mit
language:
- en
metrics:
- accuracy
- precision
- recall
- f1
library_name: keras
tags:
- finance
---
# 💰 Bank Churn Prediction — AI for Smarter Customer Retention  

![Model2_Thumbnail](https://cdn-uploads.huggingface.co/production/uploads/68faf2c58b9b8d06b47b769c/ShPfAY5zBzUUM1BRG9oga.png)

## 🧩 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
# Inference example aligned to your dataset schema

import pandas as pd
import numpy as np

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)

pred_prob = float(model.predict(X_ex, verbose=0)[0][0])
pred = int(pred_prob >= 0.5)

print(f"Churn Probability: {pred_prob:.4f}")
print("Prediction:", "Likely to churn" if pred else "Likely to stay")