Credit card fraud is a simple and inviting target. E-commerce and many other online sites have expanded the number of payment options available online, raising the danger of online fraud. Due to the rise in fraud rates, researchers began employing various machine learning approaches to detect and analyse online transaction fraud. It's critical for credit card firms to be able to spot fraudulent credit card transactions so that customers aren't charged for things they didn't buy. Such issues may be solved with Data Science, which, together with Machine Learning, cannot be underestimated. With Credit Card Fraud Detection, this project aims to demonstrate the modelling of a data set using machine learning. Modelling prior credit card transactions with data from those that turned out to be fraudulent is part of the Credit Card Fraud Detection Problem. The model is then used to determine whether or not a new transaction is fraudulent. Our goal is to detect 100% of fraudulent transactions while reducing the number of inaccurate fraud classifications. Credit Card Fraud Detection is an example of a common classification sample. On the PCA converted Credit Card Transaction data, we concentrated on analysing and pre-processing data sets, as well as deploying numerous anomaly detection techniques such as the Local Outlier Factor and Isolation Forest algorithm, as well as one class SVM (Support Vector Machine).
Software And Hardware