Which algorithms are used in this credit card default prediction project?

The system implements and compares two algorithms: XGBoost (ensemble learning) and Logistic Regression (baseline model). XGBoost achieved superior performance with 82.1% accuracy and 84.2% ROC-AUC.

What is the accuracy of the credit card default prediction model?

The XGBoost model achieves 82.1% accuracy with an F1-score of 81.6% and ROC-AUC of 84.2%, demonstrating strong predictive capability for credit risk assessment.

Who can benefit from this credit card default prediction system?

Financial institutions, banks, credit card issuers, risk managers, and credit analysts can use this system for proactive risk assessment, portfolio management, and regulatory compliance.

What technologies are used to build this project?

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

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Machine Learning

Credit Card Default Risk Prediction Using Machine Learning

The Credit Card Default Risk Prediction Using Machine Learning system is a comprehensive machine learning solution that predicts credit card default risk using customer data. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It implements XGBoost and Logistic Regression with SMOTE for class imbalance, achieving 82.1% accuracy and 84.2% ROC-AUC.

The system utilizes the UCI Credit Card Default Dataset containing 30,000 customer records with 24 features including demographic information, payment history, and billing statements. It provides a user-friendly web interface for dataset upload, exploratory data analysis, model training, and real-time risk prediction, enabling financial institutions to proactively manage credit risk and portfolio performance.

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

Key Features:

Multi-Model Default Prediction
XGBoost (82.1% Accuracy)
Logistic Regression (81.2% Accuracy)
Leak-free SMOTE Oversampling
Feature Engineering (payment ratios, delinquency counts)
Interactive Visualizations
Real-time Risk Predictions
Model Performance Comparison
Feature Importance Analysis
Flask Web Application

Algorithms Used

🔥 XGBoost

Regularized gradient boosting with n_estimators=100, max_depth=6, scale_pos_weight

🎯 Accuracy: 82.1%

📊 Logistic Regression

L2-regularized logistic regression with class_weight='balanced'

🎯 Accuracy: 81.2%

🔄 SMOTE Oversampling

Synthetic minority oversampling applied leak-free inside CV folds (k_neighbors=5)

📊 CV AUC: 0.838

🔧 Feature Engineering

Payment-to-bill ratios, delinquency counts, average bill amount

📊 Top Feature: PAY_0

Methodology & Workflow

1 Data Collection
30,000 customer records with 24 features

2 Data Preprocessing
Cleaning, encoding, feature engineering, scaling

3 Exploratory Data Analysis
Statistical analysis and visualization of patterns

4 Model Training
XGBoost and Logistic Regression with leak-free SMOTE

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

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

Model Performance Comparison

Metric	Logistic Regression	XGBoost	Best
Test Accuracy	81.2%	82.1%	XGBoost
Test Precision	80.8%	81.5%	XGBoost
Test Recall	80.5%	81.8%	XGBoost
Test F1-Score	80.6%	81.6%	XGBoost
Test ROC-AUC	83.1%	84.2%	XGBoost
CV Mean ROC-AUC	82.7%	83.8%	XGBoost

Project Package Includes:

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

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Complete Source Code

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