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--- |
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license: mit |
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language: |
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- en |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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library_name: keras |
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tags: |
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- finance |
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--- |
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# 💰 Bank Churn Prediction — AI for Smarter Customer Retention |
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## 🧩 Overview |
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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. |
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Management can concentrate efforts on improvement of service, keeping in mind these priorities. |
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**Objective** |
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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. |
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--- |
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## 🤖 Model Details |
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- **Model Type:** Feed-forward ANN (Artificial Neural Network) — Binary Classifier |
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- **Framework:** TensorFlow / Keras |
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- **Dataset:** `Churn.csv` *(Bank Customer Churn Dataset, 10,000+ customers)* |
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- **Input:** Structured customer profile (credit score, age, balance, tenure, activity, salary, etc.) |
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- **Output:** Binary churn prediction (`0 = stays`, `1 = churn`) |
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--- |
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## 📊 Data Dictionary |
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| Feature | Description | |
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|---------------------|----------------------------------------------------------------------------| |
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| `CustomerId` | Unique ID assigned to each customer | |
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| `Surname` | Customer’s last name | |
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| `CreditScore` | Customer credit history | |
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| `Geography` | Customer location | |
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| `Gender` | Gender of the customer | |
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| `Age` | Age of the customer | |
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| `Tenure` | Number of years with the bank | |
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| `NumOfProducts` | Number of bank products purchased | |
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| `Balance` | Account balance | |
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| `HasCrCard` | Whether the customer has a credit card | |
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| `EstimatedSalary` | Estimated salary | |
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| `IsActiveMember` | Whether the customer is an active member | |
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| `Exited` | Target label — 0: No (customer stays), 1: Yes (customer churns) | |
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--- |
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## 🌟 Why It Matters |
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✅ Detects early signs of potential churn |
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✅ Enables targeted retention strategies |
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✅ Improves customer engagement and loyalty |
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✅ Helps maximize profitability and reduce attrition rates |
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📘 **Full Source Notebook:** |
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The complete training and evaluation notebook is available on GitHub: |
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👉 [View on GitHub](https://github.com/joyjitroy/Machine_Learning/blob/main/Bank_Customer_Churn_Prediction_using_Artificial_Neural_Networks.ipynb) |
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--- |
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## 🚀 Example Usage |
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```python |
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# Inference example aligned to your dataset schema |
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import pandas as pd |
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import numpy as np |
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CSV_PATH = "Data/Churn.csv" |
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TARGET = "Exited" |
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# Columns exactly as in your table |
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ALL_COLS = [ |
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"CustomerId","Surname","CreditScore","Geography","Gender","Age","Tenure", |
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"NumOfProducts","Balance","HasCrCard","EstimatedSalary","IsActiveMember","Exited" |
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] |
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NUM_COLS = ["CreditScore","Age","Tenure","NumOfProducts","Balance","EstimatedSalary","HasCrCard","IsActiveMember"] |
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CAT_COLS = ["Geography","Gender"] |
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DROP_COLS = ["CustomerId","Surname"] |
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# Load data |
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df = pd.read_csv(CSV_PATH)[ALL_COLS] |
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def prepare_features(df_in, fit_cols=None): |
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X = df_in.drop(columns=[TARGET] + DROP_COLS).copy() |
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# one hot on the two categoricals |
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X = pd.get_dummies(X, columns=CAT_COLS, drop_first=True) |
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# align to training columns |
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if fit_cols is not None: |
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X = X.reindex(columns=fit_cols, fill_value=0) |
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return X |
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# If you trained in this notebook, reuse `model` and `feature_cols` from training: |
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# model.save("bank_churn_ann.keras") |
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# feature_cols = X_train.columns.tolist() |
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# If loading a saved model: |
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# from tensorflow.keras.models import load_model |
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# model = load_model("bank_churn_ann.keras") |
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# feature_cols = [...] # same list you used during training after get_dummies |
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# Single example constructed with your schema |
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example = { |
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"CustomerId": 15788241, |
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"Surname": "Smith", |
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"CreditScore": 600, |
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"Geography": "Germany", # France, Germany, Spain in this dataset |
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"Gender": "Male", # Male or Female |
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"Age": 40, |
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"Tenure": 3, |
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"NumOfProducts": 2, |
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"Balance": 60000.0, |
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"HasCrCard": 1, |
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"EstimatedSalary": 50000.0, |
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"IsActiveMember": 1, |
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"Exited": 0 # ignored at inference |
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} |
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ex_df = pd.DataFrame([example]) |
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X_ex = prepare_features(ex_df, fit_cols=feature_cols) |
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pred_prob = float(model.predict(X_ex, verbose=0)[0][0]) |
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pred = int(pred_prob >= 0.5) |
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print(f"Churn Probability: {pred_prob:.4f}") |
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print("Prediction:", "Likely to churn" if pred else "Likely to stay") |
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