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---
license: mit
language:
- en
metrics:
- accuracy
- precision
- recall
- f1
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
# 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")
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