Credit Card Fraud Detection Using Machine Learning

This project aims to develop a machine learning model for detecting fraudulent credit card transactions. By analyzing patterns in transaction data, we will train models to distinguish between legitimate and fraudulent activity. The approach involves data preprocessing, feature engineering, and applying classification algorithms such as logistic regression, decision trees, random forests, and neural networks. The goal is to improve fraud detection accuracy while minimizing false positives, ensuring a secure and efficient system for financial institutions.