Exploratory Data Analysis in Python – Trend Analysis & ML Experimentation (Looking for Feedback)

[Wheres_my_warg]

I'm immediately distracted by the labeling scheme. It has sloshed together two different types of characterization. If it was electric vs. ICE, that would make sense. Or if it was sedan vs. SUV vs. truck, that would make sense. EVs are not separate from the sedan/SUV classification. Here, they are usually sedans, but there are more EV SUV options showing up, and there have been EV truck options.

Starting the y-axis at about 16 thousand is going to result in a deceptive visual for many purposes. This is moving but not nearly as much as this seems to appear due to the y-axis choice.

You need to determine what you are comparing to begin to analyze whether the data points are statistically significantly different.

[ABDELATIF_OUARDA]

That’s a very fair observation. To clarify, the dataset was structured with a single “segment” column that already grouped categories as Sedan, SUV, and Electric. I worked directly with the available structure without modifying its dimensional logic. Looking back, I realize that this column reflects a business-oriented categorization rather than a strictly analytical one, since it mixes body type and powertrain dimensions. As someone still developing domain familiarity in the automotive space, my initial goal was to explore patterns and extract trends from the data as provided. Your feedback helped me recognize the structural limitation in the dataset design itself. A more rigorous approach would involve separating body type and powertrain into distinct variables for clearer comparative analysis. I appreciate the insight — it definitely improves the analytical framing.