Abstract
Fraudulent credit card transactions must be when customers are charged for items that they did not purchase. Such problems can be tackled with Data Science and its importance, along with Machine Learning, cannot be overstated. This project intends to illustrate the modelling of a data set using machine learning with Identifying Fraud in Online Transactions. The Identifying Fraud in Online Transactions problem includes modelling past credit card transactions with the data of the ones that turned out to be a fraud. This model is then used to recognize whether a new transaction is fraudulent or not. Our objective here is to detect 99.99% of the fraudulent transactions while minimizing the incorrect fraud classifications. Identifying Fraud in Online Transactions is a typical sample of classification. In this process, we have focused on analyzing and pre-processing data sets by using a Random Forest Algorithm.
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