I have a credit card fraud dataset. there are two populations, transactions that are fraud, and transactions that are not fraud. Can you suggest what ML algorithm I can use to model the main characteristics of these two populations. I need to create two profiles:
Fraud Transactions - transactions having an amount < 90 $, transactions happen during a particular time in the day
Transactions that are not fraud - transactions having an amount > 90 $, transactions happen during a particular time in the day.
I have used descriptive statistics, and tried to look at these two populations separately. But is there any ML model I can use to distinguish between the two distinctly like in 1) and 2)
I have more than 2 features for each population.