Case study on Cars

The dataset includes various details about vehicles, covering both technical specifications and condition information. The data consists of the manufacturer's name, model, transmission type (automatic or manual), and color, along with the odometer value and production year. It also includes engine specifications like fuel type, whether the engine has gas, the type of engine, and engine capacity. Additionally, it has details about the body type (e.g., universal, SUV, sedan, hatchback), whether a warranty is included, the vehicle's state, drivetrain, and whether it is exchangeable.

To compare graphs across various categories, I will use the guidelines we have researched, resulting in the following visualizations:

Graph #1

Engine Fuel Type by Drivetrain: Analyze if certain fuel types are associated more with drivetrains – All, Front, Rear.

Fuel Type: 5 categories (Gasoline, Diesel, Electric, Hybrid, Other)

Drivetrain: 3 categories (All, Front, Rear)

With a greater number of categories on the y-axis, the fuel types are positioned on the y-axis.

A simple bar chart is effective here since the drivetrain has only three categories, shows how each fuel type relates to each drivetrain.

A stacked bar graph can be a second option to show the breakdown of fuel types for each drivetrain.

Graph #2

Transmission Type by Manufacturer: See if certain manufacturers favor automatic or manual transmissions.

Manufacturer Name: More than 50 categories

Transmission Type: 2 categories (Automatic, Manual)

Since there are many manufacturers, the manufacturer names are best placed on the x-axis, with a stacked bar chart representing transmission types. Since there are more categories, stacked graph would be the first choice for this data.

Alternatively, a standard bar chart can be used, though it may be harder to interpret due to the high number of categories.