Credit Card Fraud Detection

This project focuses on detecting fraudulent credit card transactions in a highly imbalanced dataset.

🔍 Problem Statement

Develop a machine learning model that accurately classifies fraud cases from legitimate transactions using anonymized data.

📁 Data

Dataset used: Kaggle Credit Card Fraud Dataset
Contains 284,807 transactions with 492 fraud cases (~0.17%)

⚙️ Approach

• Preprocessing: Dropped 'Time', scaled 'Amount'
• Handled class imbalance using SMOTE and ADASYN
• Models: Logistic Regression, Random Forest, XGBoost
• Cross-validation with stratified folds
• ROC-AUC used as primary metric

🏆 Results

• Best ROC-AUC: ~0.977 (XGBoost + threshold tuning)
• Balanced precision/recall with custom threshold

🧰 Tech Stack

Python, Pandas, Scikit-learn, XGBoost, Imbalanced-learn, Matplotlib, Seaborn