ABSTRACT:

Credit score cards are a common risk control method in the financial industry. It uses personal information and data submitted by credit card applicants to predict the probability of future defaults and credit card borrowings. The bank is able to decide whether to issue a credit card to the applicant. The task is to build a machine learning model to predict if an applicant is 'good' or 'bad' client.

DATASET DESCRIPTION:

The dataset is taken from kaggle. The number of rows within the dataset is 438557 and the number of columns is 18. The data is in the form of Feature_name - Explanation

ID - Client number
CODE_GENDER - Gender
FLAG_OWN_CAR - Is there a car
FLAG_OWN_REALTY - Is there a property
CNT_CHILDREN - Number of children
AMT_INCOME_TOTAL - Annual income
NAME_INCOME_TYPE - Income category
NAME_EDUCATION_TYPE - Education level
NAME_FAMILY_STATUS - Marital status
NAME_HOUSING_TYPE - Way of living
DAYS_BIRTH - Birthday
DAYS_EMPLOYED - Start date of employment
FLAG_MOBIL - Is there a mobile phone
FLAG_WORK_PHONE - Is there a work phone
FLAG_PHONE - Is there a phone
FLAG_EMAIL - Is there an email
OCCUPATION_TYPE - Occupation
CNT_FAM_MEMBERS - Family size

DATASET SOURCE: https://www.kaggle.com/rikdifos/credit-card-approval-prediction