Abstract: It's vital that mastercard companies are ready to identify fraudulent credit card transactions so that customers are not charged for items that they didn't purchase. Such problems are often tackled with Data Science and its importance, along with Machine Learning, cannot be overstated. This project intends for instance the modelling of a knowledge set using machine learning with mastercard Fraud Detection. The Credit Card Fraud Detection Problem includes modelling past credit card transactions with the info of those that clothed to be fraud. This model is then wont to recognize whether a replacement transaction is fraudulent or not. Our objective here is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications. Credit Card Fraud Detection is a typical sample of classification. In this process, we have focused on analysing and pre-processing data sets also because the deployment of multiple anomaly detection algorithms such as Local Outlier Factor and Isolation Forest algorithm on the Principle Component Analysis transformed Credit Card Transaction data.Keywords— Credit card fraud, applications of machine learning, data science, isolation forest algorithm, local outlier factor, automated fraud detection.