make_mtcars_check_data: MtCars Dataset

The make_mtcars_check_data function generates a synthetic version of the famous Motor Trend Car Road Tests (MtCars) dataset. This dataset is widely used for teaching and testing linear regression concepts, particularly for predicting fuel efficiency (mpg) based on vehicle characteristics.

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Overview

This utility provides a reproducible, MtCars-shaped dataset:

• Structure: 32 observations (cars) with 11 variables.
• Relationships: Realistic correlations, such as mpg decreasing as hp (horsepower) and wt (weight) increase.
• Purpose: Excellent for testing regression models, feature selection, and data visualization techniques.
• Reproducibility: Uses a fixed seed (default 42).

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Parameters

Parameter	Type	Description	Default
seed	int	Random seed for reproducibility.	42

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Returns

Return	Type	Description
data	numpy.ndarray	Array of shape (32, 11) containing vehicle specifications.
column_names	list[str]	List of column names: ['mpg', 'cyl', 'disp', 'hp', 'drat', 'wt', 'qsec', 'vs', 'am', 'gear', 'carb'].
car_names	list[str]	List of placeholder car names (e.g., 'Car 1', 'Car 2') matching the rows.

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Columns Description

Column	Description
mpg	Miles/(US) gallon
cyl	Number of cylinders
disp	Displacement (cu.in.)
hp	Gross horsepower
drat	Rear axle ratio
wt	Weight (1000 lbs)
qsec	1/4 mile time
vs	Engine (0 = V-shaped, 1 = straight)
am	Transmission (0 = automatic, 1 = manual)
gear	Number of forward gears
carb	Number of carburetors

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Example Usage

from machinegnostics.data import make_mtcars_check_data

# Generate MtCars data
data, cols, names = make_mtcars_check_data()

print(f"Shape: {data.shape}")
# Output: (32, 11)

print(f"Key Features: {cols[3]} (HP), {cols[5]} (Weight)")
# Output: Key Features: hp (HP), wt (Weight)

# Predict MPG (column 0)
X = data[:, [3, 5]] # hp, wt
y = data[:, 0]      # mpg