Car Loan Synthetic Binary Classification Dataset

NannyML provides a synthetic dataset describing a binary classification problem, to make it easier to test and document its features.

To find out what requirements NannyML has for datasets, check out Data Requirements.

Problem Description

The dataset describes a machine learning model that predicts whether a customer will repay a loan to buy a car.

A sample of the dataset can be seen below.

>>> import nannyml as nml
>>> reference, analysis, analysis_targets = nml.load_synthetic_car_loan_dataset()
>>> display(reference.head(3))

	car_value	salary_range	debt_to_income_ratio	loan_length	repaid_loan_on_prev_car	size_of_downpayment	driver_tenure	y_pred_proba	y_pred	repaid	timestamp
0	39811	40K - 60K €	0.63295	19	False	40%	0.212653	0.99	1	1	2018-01-01 00:00:00.000
1	12679	40K - 60K €	0.718627	7	True	10%	4.92755	0.07	0	0	2018-01-01 00:08:43.152
2	19847	40K - 60K €	0.721724	17	False	0%	0.520817	1	1	1	2018-01-01 00:17:26.304

The model uses 7 features:

car_value: A numerical feature representing the price of the car.
salary_range: A categorical feature with 4 categories that identify the range the employee’s yearly income falls within.
debt_to_income_ratio: A numerical feature representing the ratio of debt to income from the customer.
loan_length: A numerical feature representing in how many months the customer wants to repay the loan.
repaid_loan_on_prev_car: A categorical feature with 2 categories, stating whether the customer repaid or not a previous loan.
size_of_downpayment: A categorical feature with 10 categories, representing the percentage in increments of 10% of the size of the downpayment of the car value.
tenure: A numerical feature describing how many years the costumer has been driving.

There are 3 columns that reference the output of the model:

y_pred_proba: The model predicted probability of the customer repaying the loan.
y_pred: The model prediction in binary form.
repaid: The Target column describing if the customer actually repaid the loan.

There is also an auxiliary column that is helpful but not used by the monitored model: