Project Description

Credit card fraud detection is currently the most frequently occurring problem all over the world. This is mainly because of online transaction , businesses and e commerce platforms .There is enormous usage of credit card because of online shopping etc . Credit card fraud occurs when someone uses the details of that the transaction , or unknowingly our money is debited than others money because of some typing mistakes etc.... .Such problems can be accomplish with machine learning. The project main objective is to detect 100% of the fraud transactions . In this supervised machine learning there are different types of methods like regression, classification etc . Credit card fraud detection comes under classification .By using this the transaction can classify whether it is true or false (fraud or not) .We used five machine learning algorithms 1.navieBayes 2.knearestneighbours 3.logistic regression 4.Support vector machine 5.random forest . In this process we have focused more on preprocessing of data set , analysing and building the model using the algorithms. By using these algorithms on our dataset we find accuracy using confusion matrix .and we analyze all the algorithms we have taken .