Data Description:

Below are the variables and their corresponding description for the credit risk dataset. The target variable is class that predicts whether an instance of credit is bad or good

Column name	Description
checking_status	Status of existing checking account, in Deutsche Mark
duration	Duration in months
credit_history	Credit history (credits taken, paid back duly, delays, critical accounts)
purpose	Purpose of the credit (car, television,...)
credit_amount	Credit amount
savings_status	Status of savings account/bonds, in Deutsche Mark
employment	Present employment, in number of years
installment_commitment	Installment rate in percentage of disposable income
personal_status	Personal status (married, single,...) and sex
other_parties	Other debtors / guarantors
residence_since	Present residence since X years
property_magnitude	Property (e.g. real estate)
age	Age in years
other_payment_plans	Other installment plans (banks, stores)
housing	Housing (rent, own,...)
existing_credits	Number of existing credits at this bank
job	Job
num_dependents	Number of people being liable to provide maintenance for
own_telephone	Telephone (yes,no)
foreign_worker	Foreign worker (yes,no)
class	Target variable (1, 0) - 1 = good, 0 = bad

Saving Prediction File & Sample Submission

You can find more details on how to save a prediction file here: https://discuss.dphi.tech/t/how-to-submit-predictions/548

Sample submission: You should submit a CSV file with a header row. A sample submission of the first few rows can be found below:

prediction
1
1
0
1
0
1
1

Note: The header name should be ‘prediction’ else it will throw an evaluation error.

Data Sprint 50 - Credit Risk

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