Bank Customer Churn Prediction – IT and Computer Engineering Guide
1. Project Overview
Objective: Predict customer churn for a bank using
historical data and machine learning techniques.
Scope: Help banks identify customers likely to leave, enabling targeted
retention strategies.
2. Prerequisites
Knowledge: Understanding of Python, machine learning, and
data preprocessing techniques.
Tools: Python, Scikit-learn, Pandas, NumPy, Matplotlib/Seaborn for
visualization, and possibly TensorFlow or PyTorch.
Data: A dataset containing customer demographics, transaction history, and
account details (e.g., Kaggle's Bank Churn Dataset).
3. Project Workflow
- Data Collection: Gather a dataset containing customer details and churn labels.
- Data Preprocessing: Handle missing data, encode categorical variables, and normalize numeric features.
- Exploratory Data Analysis: Visualize trends, correlations, and identify important features.
- Model Training: Train machine learning models like Logistic Regression, Random Forest, or Gradient Boosting.
- Evaluation: Evaluate the model using metrics such as accuracy, precision, recall, and AUC-ROC.
- Deployment: Develop a dashboard or API for business integration.
4. Technical Implementation
Step 1: Import Libraries
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, roc_auc_score
Step 2: Load and Preprocess Data
# Load dataset
data = pd.read_csv('customer_churn.csv')
# Encode categorical variables
encoder = LabelEncoder()
data['Gender'] = encoder.fit_transform(data['Gender'])
data['Geography'] = encoder.fit_transform(data['Geography'])
# Scale numeric features
scaler = StandardScaler()
scaled_features = scaler.fit_transform(data[['CreditScore', 'Age', 'Balance',
'EstimatedSalary']])
# Split data
X = data.drop(columns=['Churn'])
y = data['Churn']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=42)
Step 3: Train a Classification Model
# Train Random Forest Classifier
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
Step 4: Evaluate the Model
# Make predictions and evaluate
y_pred = model.predict(X_test)
y_prob = model.predict_proba(X_test)[:, 1]
print(classification_report(y_test, y_pred))
print('AUC-ROC:', roc_auc_score(y_test, y_prob))
5. Results and Insights
Examine the model's performance metrics to assess accuracy and reliability. Identify key factors contributing to churn.
6. Challenges and Mitigation
Data Imbalance: Use oversampling techniques like SMOTE or
undersampling to handle class imbalance.
Feature Importance: Regularly monitor and update feature importance as customer
behavior evolves.
7. Future Enhancements
Incorporate advanced models like XGBoost or Neural Networks
for improved predictions.
Implement a real-time monitoring system for live customer behavior tracking.
8. Conclusion
The Bank Customer Churn Prediction project demonstrates how machine learning can proactively identify at-risk customers, aiding in targeted retention efforts.