🏦 Bank Customer Churn Prediction

This is an end-to-end machine learning project to predict customer churn using a real-world inspired bank dataset. The project focuses on understanding why customers leave the bank and building an interpretable logistic regression model to identify customers at risk of churning.

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📌 Project Highlights

• 🔍 Exploratory Data Analysis (EDA)
• 🧼 Data Cleaning & Outlier Detection
• 🧠 Feature Engineering (Engagement score, loyalty ratio, etc.)
• 🚩 Outlier & Underbanked Flags
• 📊 Logistic Regression Model with Balanced Class Weights
• 📈 Model Evaluation: Precision, Recall, F1, ROC AUC
• 💬 Business Insights and Churn Strategy Recommendations

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📂 Dataset Overview

The dataset includes the following features:

• Demographics: age, gender, country
• Bank interaction: tenure, balance, estimated_salary
• Product usage: num_of_products, has_credit_card, is_active_member
• Engineered features: engagement_score, balance_salary_ratio, loyalty_score, etc.
• Target: churn (0 = stayed, 1 = churned)

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🧠 Feature Engineering

Created several insightful features such as:

• engagement_score = activity + credit card usage + product count
• loyalty_score = tenure / (age + 1)
• balance_salary_ratio = balance / (estimated_salary + 1)
• Flags for high-value customers, underbanked users, and low credit score holders

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🤖 Model: Logistic Regression

• Trained with and without class balancing
• Balanced model improved recall for churners from 31% ➜ 73%
• ROC AUC = 0.81 (strong discriminatory power)

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📊 Evaluation

• Confusion matrix and classification report used to assess performance
• Feature importances extracted for interpretability
• Model and scaler saved for future predictions

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🔮 What's Next

• Train Random Forest and XGBoost for performance comparison
• Visualize SHAP values for model transparency
• Build a Streamlit app or dashboard
• Generate customer churn probability scores and prioritize high-risk users

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🚀 Run Locally

1. Clone the repo

git clone https://github.com/bhandeystruck/bank-churn-prediction.git

2. Install requirements

pip install -r requirements.txt

3. Run the notebook or use the saved model for predictions

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📬 Contact

Created by Aditya Bhandari · bhandeystruck@gmail.com

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📄 License

This project is open source and available under the MIT License.