Banking Fraud Detection using Python and Machine Learning

Credit card companies must have the ability to identify fraudulent credit card transactions in order to stop customers from being charged for goods they did not purchase. Data Science may be used to solve these issues, and coupled with machine learning, it is of utmost relevance. This study aims to demonstrate how machine learning can be used to model a data set by using the detection of credit card fraud as an example. The "Credit Card Fraud Detection Problem" includes modelling prior credit card transactions using data from those that were subsequently shown to be fraudulent. The outcome of this analysis is used to assess if a new transaction is fraudulent or not. The goal here is to minimize inaccurate fraud categories while detecting 100% of the fraudulent transactions. The identification of "credit card fraud" is an excellent illustration of classification. In this process, we have focused on data analysis and preparation. We have also used a number of aberration detection techniques, such as the "Local Outlier Factor" and "Isolation Forest" methods, on PCA transformed "Credit Card Transaction" data