Business Problem: Banks receives so many applications for credit card (CC) request. Going through each request manually can be very time consuming, also prone to human errors. However, if we can use the historical data to build a model which can shortlist the candidates for approval that save time.

The provided training dataset contains past applications for CC. Each row in the dataset includes information on the applicant  (sex,age,Debt, Married etc.)  and the output is the decision (1: approved, 0: denied) 

**Goal**
By using any ML classifier,  you must build a predictor model that predicts if a CC should be approved for a person that has certain characteristics (sex,age,Debt, Married etc.)