Synthetic Regression Dataset

NannyML provides a synthetic dataset describing a regression 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 tries to predict the price of a used car.

Dataset Description

A sample of the dataset can be seen below.

>>> import nannyml as nml
>>> reference_df, analysis_df, analysis_targets_df = nml.datasets.load_synthetic_car_price_dataset()
>>> display(reference_df.head())

	car_age	km_driven	price_new	accident_count	door_count	fuel	transmission	y_true	y_pred	timestamp
0	15	144020	42810	4	3	diesel	automatic	569	1246	2017-01-24 08:00:00.000
1	12	57078	31835	3	3	electric	automatic	4277	4924	2017-01-24 08:00:33.600
2	2	76288	31851	3	5	diesel	automatic	7011	5744	2017-01-24 08:01:07.200
3	7	97593	29288	2	3	electric	manual	5576	6781	2017-01-24 08:01:40.800
4	13	9985	41350	1	5	diesel	automatic	6456	6822	2017-01-24 08:02:14.400

The model uses 7 features:

• car_age - a numerical feature. The age of the car in years.

• km_driven - a numerical feature. The number of kilometers a car has drived.

• price_new - a numerical feature. The price of the car in Euros when it was new.

• accident_count - a numerical feature. The number of accidents the car has been involved in.

• door_count - a numerical feature. The number of doors the car has. If it is a hatchback, the door count is increased by 1.

• fuel - a categorical feature describing whether the car uses gas, diesel or electricity as fuel.

• transmission - a categorical feature describing whether the car uses manual or automatic transmission.

The model predicts the predicted price of the car at the y_pred column. The y_true is the Target column describing the actual value of the car.

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

• timestamp - a date column informing us of the date the prediction was made.