Credit Card Default Prediction using Support Vector Machine (SVM)

A machine learning project that predicts credit card payment defaults using Support Vector Machine classification with comprehensive data preprocessing and model optimization.

📊 Project Overview

This project analyzes credit card client data to predict whether a customer will default on their next payment. The model uses various customer attributes including demographics, payment history, and billing information to make accurate predictions.

🎯 Objective

• Predict credit card payment defaults using historical customer data
• Implement and optimize Support Vector Machine for binary classification
• Handle real-world data challenges including missing values and categorical encoding
• Achieve balanced performance on both default and non-default predictions

📋 Dataset Information

Source: Default of Credit Card Clients Dataset

• Total Records: 30,000 customers
• Features: 24 attributes (after preprocessing)
• Target Variable: Default payment next month (Binary: 0=No Default, 1=Default)

Key Features:

• LIMIT_BAL: Credit limit amount
• Demographics: Sex, Education, Marriage, Age
• Payment History: PAY_0 to PAY_6 (6 months of payment status)
• Bill Amounts: BILL_AMT1 to BILL_AMT6 (6 months of billing)
• Payment Amounts: PAY_AMT1 to PAY_AMT6 (6 months of payments)

Feature Encoding:

• SEX: 1=Male, 2=Female
• EDUCATION: 1=Graduate, 2=University, 3=High School, 4=Others
• MARRIAGE: 1=Married, 2=Single, 3=Others
• Payment Status: -1=Pay Duly, 1-9=Payment Delay (months)

🛠️ Technical Implementation

Libraries Used

pandas              # Data manipulation and analysis
numpy               # Numerical computing
matplotlib          # Data visualization
scikit-learn        # Machine learning algorithms
 - SVM             # Support Vector Machine
 - GridSearchCV    # Hyperparameter tuning
 - train_test_split# Data splitting
 - preprocessing   # Data scaling and encoding
 - metrics         # Model evaluation