# 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()
```

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.