The popularity of online shopping is growing day by day. In financial year 2021, over 40 billion digital
transactions worth more than a quadrillion Indian rupees were recorded across the country. As the number of credit card users
rise world- wide, the opportunities for attackers to steal credit card details and subsequently, commit fraud are also increasing.
Since humans tend to exhibit specific behavioristic profiles, every cardholder can be represented by a set of patterns containing
information about the typical purchase category, the time since the last purchase, the amount of money spent etc. So these
frauds can be detected through various algorithms mainly random forest and logistic regression. To enhance the boost and build
model with much more efficiency adaboost is also added.
