CREDIT CARD FRAUD DETECTION

It is essential for credit card companies to identify fraudulent transactions so that customers are not billed for purchases they never made. Data Science and Machine Learning play a crucial role in addressing this challenge. This project demonstrates the modeling of a dataset for Credit Card Fraud Detection using machine learning. The problem involves analyzing past credit card transactions, distinguishing between legitimate and fraudulent ones, and then building a model capable of detecting whether a new transaction is fraudulent.The main objective is to detect all fraudulent transactions while minimizing the number of legitimate transactions incorrectly classified as fraud. Since this is a classification problem, our approach involves analyzing and preprocessing the dataset, followed by applying multiple anomaly detection algorithms, such as the Local Outlier Factor (LOF) and the Isolation Forest algorithm, on the PCA-transformed credit card transaction data.