Credit Card Fraud Detection

Credit card fraud is a major financial problem that affects both banks and customers. The goal of this project is to build a machine learning model capable of detecting fraudulent credit card transactions.

The dataset used contains real-world transactions made by European cardholders. Due to the highly imbalanced nature of the data, special preprocessing techniques and evaluation metrics are required.

In this project, we applied data preprocessing, handled class imbalance using SMOTE, and implemented a Random Forest classifier to detect fraudulent transactions. The model was evaluated using precision, recall, F1-score, and a confusion matrix.

The Random Forest model achieved a high recall score for the fraud class, demonstrating its effectiveness in identifying fraudulent transactions.