Online transaction fraud detection using machine learning Available to Purchase

This project aims to detect online payment fraud using machine learning algorithms. Fraud is an illegal criminal activity carried out for monetary or personal gain. Detection of such fraud requires a dataset comprising details about past fraudulent transactions for training, testing, and pattern detection to anticipate any fraud. For this task, authors are using a dataset from Kaggle and have been implementing four algorithms viz. logistic regression, decision trees, random forests, and k-nearest neighbor. However, the data must be cleaned and pre-formatted before training and testing. Therefore, authors perform data pre-processing which removes attributes that do not help to identify fraudulent transactions, handle missing data and outliers, match or encode categorical data, and more. This allows the implementation of a training model to predict fraudulent or non-fraudulent transactions. Finally, the accuracy of all four models is compared and it is noticed that all 4 models are performing quite well for fraud detection.