Abstract: The main objective of this project is to develop a Credit card fraud Detection using the Random Forest Algorithm. Recently, a dramatic spike in the number of credit card purchases has contributed to a substantial uptick in fraudulent activity. Implementation of successful fraud prevention mechanisms has been necessary for all banks issuing credit cards to reduce their losses. This makes it difficult for the retailer to check whether or not the client who is making a transaction is the genuine cardholder. With the proposed method, the accuracy of detecting the fraud can be increased using random forest algorithm. Random forest algorithm classification method for study of data collection and actual consumer dataset. Finally optimize the precision of the data on the test. The techniques efficiency is judged based on accuracy, flexibility, specificity and precision. Then the analysis of some of the given attributes determines the identification of fraud and gives visualization of the graphical model. The performance of the techniques is measured based on precision, flexibility, specificity and accuracy.

Keywords: Credit card, Random Forest algorithm, Machine Learning, Decision Tree, and Classifier.

| DOI: 10.17148/IARJSET.2020.7903