Banking Customer Churn Prediction

Predicting customer churn using machine learning to enable proactive retention strategies and reduce revenue loss for banking institutions.

Business Problem

Customer churn is a critical challenge in the banking sector, with acquiring new customers costing 5-25 times more than retaining existing ones. This project addresses the need for early identification of at-risk customers, enabling banks to:

• Reduce Revenue Loss: Predict churners before they leave, allowing timely intervention
• Optimize Marketing Spend: Target retention campaigns to high-risk customers
• Improve Customer Lifetime Value: Implement personalized retention strategies
• Resource Allocation: Focus relationship managers on accounts most likely to churn

Key Findings

Model Performance

Model	Training Accuracy	Testing Accuracy	F1 Score	Precision	Recall
Decision Tree	89.99%	84.65%	0.85	0.85	0.85
Random Forest	90.47%	85.00%	0.85	0.85	0.85

Random Forest demonstrated superior generalization with balanced performance across all metrics.

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Critical Business Insights

Customer Churn Distribution

79.6% customers retained vs 20.4% churned — SMOTE applied to balance dataset.

Geography & Product Impact on Churn

Germany shows 2× higher churn rate; two-product customers are most loyal.

Product Strategy: Customers with exactly 2 products have the lowest churn rate. Single-product customers show significantly higher attrition.

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Feature Importance Analysis

Feature Importance — Top Predictors of Churn

Age, Account Balance, and Geography dominate churn prediction.

Top Predictors:

1. Age (log-transformed): Older customers show higher churn propensity
2. Account Balance: Zero-balance accounts are strong churn indicators
3. Geography: Regional factors significantly impact retention
4. Number of Products: Product engagement inversely correlates with churn

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Methodology

Data Overview

• Dataset: 10,000 customer records from banking institution
• Features: 14 variables including demographics, account details, and banking behavior
• Target: Binary classification (Churned: Yes/No)
• No Data Leakage: Zero duplicate records, no missing values

Preprocessing Pipeline

1. Feature Engineering

   • Categorized NumOfProducts: Single, Two, More than 2
   • Binary transformation of Balance: Zero vs Non-zero
   • Removed irrelevant identifiers (RowNumber, CustomerId, Surname)

2. Data Transformation

   • Log-normal transformation on Age to correct right skewness
   • One-Hot Encoding for categorical variables
   • Label encoding for target variable

3. Class Balancing

   • Applied SMOTE (Synthetic Minority Over-sampling Technique)
   • Balanced training set: 6,356 samples per class

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Model Development

Decision Tree

• GridSearchCV hyperparameter tuning (5-fold CV)
• Optimized parameters: entropy criterion, max_depth=9, min_samples_leaf=2

Random Forest (Selected Model)

• Ensemble of 50 decision trees
• Parameters: max_depth=8, min_samples_leaf=3, class_weight='balanced'
• Superior generalization and robustness to overfitting

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Model Evaluation

Confusion Matrix & ROC-AUC Curve

Strong separation with ROC-AUC = 0.86 indicating robust model discrimination.

Confusion Matrix Analysis:

• True Negatives: 1,471 (correctly identified non-churners)
• False Positives: 136 (acceptable over-prediction for proactive intervention)
• False Negatives: 164 (critical misses requiring model refinement)
• True Positives: 229 (successfully identified churners)

ROC-AUC Score: 0.86 indicating strong discriminative ability

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Business Recommendations

Immediate Actions

1. Product Cross-Selling: Incentivize single-product customers to adopt a second product through bundled offerings
2. Zero-Balance Monitoring: Implement automated alerts for accounts approaching zero balance
3. Regional Strategy: Investigate and address specific pain points in German market
4. Age-Targeted Retention: Design age-specific retention programs for high-risk segments

Long-Term Strategy

• Predictive Scoring: Deploy model to generate monthly churn risk scores
• Intervention Campaigns: Allocate retention budget based on predicted churn probability
• A/B Testing: Validate intervention effectiveness on model-identified high-risk customers
• Continuous Learning: Retrain model quarterly with new data to maintain accuracy

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Technologies Used

Data Processing: Pandas, NumPy
Machine Learning: Scikit-learn, Imbalanced-learn (SMOTE)
Visualization: Matplotlib, Seaborn
Model Selection: GridSearchCV with 5-fold cross-validation

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Results Summary

The Random Forest classifier achieves 85% accuracy in predicting customer churn, with balanced precision-recall tradeoff suitable for business deployment. Feature analysis reveals actionable insights:

• Product engagement is the most controllable predictor
• Geographic disparities require localized strategies
• Zero-balance accounts need proactive engagement
• Age-based segmentation enables targeted interventions

Expected Impact: Implementing model-driven retention strategies could reduce churn by 30-40%, translating to significant revenue protection and improved customer lifetime value.

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Dataset

Source: Banking Customer Churn Dataset
Records: 10,000 customers
Period: Cross-sectional snapshot
Geography: France, Germany, Spain

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Author: Shree Koshti
Contact: Email | LinkedIn | GitHub

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This project demonstrates the application of machine learning to solve real-world business problems in the banking sector, providing actionable insights for customer retention strategy.