Dataset Title: Motor Trend Car Road Tests (mtcars) Description: The data was extracted from the 1974 Motor Trend US magazine and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models). It is a classic, foundational dataset used extensively in statistics and data science for learning exploratory data analysis, regression modeling, and hypothesis testing.

This dataset is a staple in the R programming language (?mtcars) and is now provided here in a clean CSV format for easy access in Python, Excel, and other data analysis environments.

Acknowledgements: This dataset was originally compiled and made available by the journal Motor Trend in 1974. It has been bundled with the R statistical programming language for decades, serving as an invaluable resource for learners and practitioners alike.

Data Dictionary: Each row represents a different car model. The columns (variables) are as follows:

Column Name Data Type Description model object (String) The name and model of the car. mpg float Miles/(US) gallon. A measure of fuel efficiency. cyl integer Number of cylinders (4, 6, 8). disp float Displacement (cubic inches). Engine size. hp integer Gross horsepower. Engine power. drat float Rear axle ratio. Affects torque and fuel economy. wt float Weight (1000 lbs). Vehicle mass. qsec float 1/4 mile time (seconds). A measure of acceleration. vs binary Engine shape (0 = V-shaped, 1 = Straight). am binary Transmission (0 = Automatic, 1 = Manual). gear integer Number of forward gears (3, 4, 5). carb integer Number of carburetors (1, 2, 3, 4, 6, 8). Key Questions & Potential Use Cases: This dataset is perfect for exploring relationships between a car's specifications and its performance. Some classic analysis questions include:

Fuel Efficiency: What factors are most predictive of a car's miles per gallon (mpg)? Is it engine size (disp), weight (wt), or horsepower (hp)?

Performance: How does transmission type (am) affect acceleration (qsec) and fuel economy (mpg)? Do manual cars perform better?

Classification: Can we accurately predict the number of cylinders (cyl) or the type of engine (vs) based on other car features?

Clustering: Are there natural groupings of cars (e.g., performance cars, economy cars) based on their specifications?

Inspiration: This is one of the most famous datasets in statistics. You can find thousands of examples, tutorials, and analyses using it online. It's an excellent starting point for:

Practicing multiple linear regression and correlation analysis.

Building your first EDA (Exploratory Data Analysis) notebook.

Learning about feature engineering and model interpretation.

Comparing statistical results from R and Python (e.g., statsmodels vs scikit-learn).

File Details: mtcars-parquet.csv: The main dataset file in CSV format.

Number of instances (rows): 32

Number of attributes (columns): 12

Missing Values? No, this is a complete dataset.

Dataset

This dataset was created by Sudhanshu Chaturvedi

Contents

CONTEXT: This is a dataset of electric vehicles.

One of the more popular data science datasets is the mtcars dataset. It is known for its simplicity when running analysis and visualizations.

When looking for simple datasets on EVs, there don't seem to be any. Also, given the growth in this market, this is something many would be curious about. Hence, the reason for creating this dataset.

For more information, please visit the data source below.

TASKS: Some basic tasks would include 1. Which car has the fastest 0-100 acceleration? 2. Which has the highest efficiency? 3. Does a difference in power train effect the range, top speed, efficiency? 4. Which manufacturer has the most number of vehicles? 5. How does price relate to rapid charging?

CONTENT: I've included two datasets below:

1. 'ElectricCarData_Clean.csv' -- original pulled data.

2. 'ElectricCarData_Norm.csv' -- units removed from each of the rows -- rapid charge has a binary yes/no value

The point of both is to have users practice some data cleaning.

CREDITS: There are two credits and sourcing that needs to be mentioned: 1. Datasource: ev-database.org/ 2.*Banner image*: freepik - author - 'macrovector'

UPDATES: There will be future updates when we can attain additional data.