A Clustering project in Python that aims to clustered loans into several types of loan clusters.
Variable - Description LN_ID - Loan ID
TARGET - "Target variable (1 - client with late payment more than X days - 0 - all other cases)"
CONTRACT_TYPE - Identification if loan is cash or revolving
GENDER - Gender of the client
NUM_CHILDREN - Number of children the client has
INCOME - Monthly income of the client
APPROVED_CREDIT - Approved credit amount of the loan
ANNUITY - Loan annuity (amount that must be paid monthly)
PRICE - For consumer loans it is the price of the goods for which the loan is given
INCOME_TYPE - "Clients income type (businessman - working - maternity leave - …)"
EDUCATION - The client highest education
FAMILY_STATUS - Family status of the client
HOUSING_TYPE - "What is the housing situation of the client (renting - living with parents - ...)"
DAYS_AGE - Client's age in days at the time of application
DAYS_WORK - How many days before the application the person started current job
DAYS_REGISTRATION - How many days before the application did client change his registration
DAYS_ID_CHANGE - How many days before the application did client change the identity document with which he applied for the loan
WEEKDAYS_APPLY - On which day of the week did the client apply for the loan
HOUR_APPLY - Approximately at what hour did the client apply for the loan
ORGANIZATION_TYPE - Type of organization where client works
EXT_SCORE_1 - Normalized score from external data source
EXT_SCORE_2 - Normalized score from external data source
EXT_SCORE_3 - Normalized score from external data source