Which algorithms are used in this bank customer churn prediction project?

The system implements and compares two algorithms: Logistic Regression (interpretable) and Decision Tree (non-linear). Decision Tree achieved superior performance with 89.3% accuracy and 0.934 ROC-AUC.

What is the accuracy of the bank churn prediction model?

The Decision Tree model achieves 89.3% accuracy with an F1-Score of 0.891 and ROC-AUC of 0.934, demonstrating excellent predictive capability for customer churn detection.

Who can benefit from this bank churn prediction system?

Banking institutions, retail banks, customer retention teams, marketing professionals, and financial analysts can use this system for proactive retention strategies and customer lifetime value optimization.

What technologies are used to build this project?

The project is built using Python with Pandas, NumPy, Scikit-learn, and imbalanced-learn for machine learning. The web interface is developed using Flask with HTML, CSS, and JavaScript.

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Machine Learning

Intelligent Bank Customer Churn Prediction Using Machine Learning

The Intelligent Bank Customer Churn Prediction Using Machine Learning system is a comprehensive machine learning solution that predicts customer churn in the banking sector using the Churn_Modelling dataset. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It implements Logistic Regression and Decision Tree with SMOTE for class imbalance, achieving 89.3% accuracy and 0.934 ROC-AUC.

The system utilizes the Churn_Modelling dataset containing 10,000 customer records with 14 features including demographics, account details, and behavioral indicators. It provides a user-friendly web interface for data upload, exploratory data analysis, model training, and real-time churn prediction, enabling banks to proactively identify at-risk customers and implement targeted retention strategies.

Python 3.8+ Machine Learning Logistic Regression Decision Tree Scikit-learn SMOTE Pandas NumPy Matplotlib Seaborn Flask HTML/CSS/JS

Key Features:

Multi-Model Churn Prediction
Decision Tree (89.3% Accuracy)
Logistic Regression (86.2% Accuracy)
Feature Engineering (Balance_to_Salary_Ratio, age/tenure categories)
Leak-free SMOTE Cross-Validation
Interactive Visualizations
Real-time Churn Predictions
Model Performance Comparison
Feature Importance Analysis
Flask Web Application

Algorithms Used

📊 Logistic Regression

Linear classifier with L2 regularization, class_weight='balanced', max_iter=1000

🎯 Accuracy: 86.2%

🌳 Decision Tree

Non-linear tree-based classifier with max_depth=10, min_samples_split=5, class_weight='balanced'

🎯 Accuracy: 89.3%

🔧 Feature Engineering

Balance_to_Salary_Ratio, age_category, tenure_category, has_multiple_products

📊 Top Feature: Age

🔄 SMOTE Oversampling

Synthetic minority oversampling applied leak-free inside CV folds

📊 CV Mean: 92.6%

Methodology & Workflow

1 Data Collection
10,000 customer records with 14 features

2 Data Preprocessing
Cleaning, encoding, feature engineering, scaling

3 Exploratory Data Analysis
Statistical analysis and visualization of churn patterns

4 Model Training
Logistic Regression and Decision Tree with leak-free SMOTE

5 Model Evaluation
Accuracy, Precision, Recall, F1-Score, ROC-AUC

6 Web Deployment
Flask web app with real-time churn prediction

Model Performance Comparison

Metric	Logistic Regression	Decision Tree	Best
Test Accuracy	86.2%	89.3%	Decision Tree
Test Precision	85.7%	89.1%	Decision Tree
Test Recall	86.2%	89.3%	Decision Tree
Test F1-Score	85.9%	89.1%	Decision Tree
Test ROC-AUC	92.8%	93.4%	Decision Tree
CV Mean ROC-AUC	92.4%	92.6%	Decision Tree

Project Package Includes:

Complete Source Code Documentation (50+ pages) Video Tutorial Churn_Modelling Dataset Flask Web App Model Files (Pickle) Visualizations 24/7 Expert Support

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