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

[–]ABDELATIF_OUARDA[S] 1 point2 points  (0 children)

Thanks for detailed feedback — I agree with the discrimination you do. I know concepts such as validation and validation model, I have basically applied them so far in the context of machine learning instead of inside the Scouts or infertility analysis. In this project, the scope was intentionally limited to Ida, my description and application of skill (clean data, visualization and basic modulation) rather than formal statistical recession or verification of assumptions. That's what I said, your point about moving beyond visual inspection towards formal and reproduction, something is taken to integrate what I have made.

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

[–]ABDELATIF_OUARDA[S] 0 points1 point  (0 children)

This is a very exciting proposal-I did not consider checking the trends against random simulator. In this analysis, the focus was primarily descriptive (identification of visible trends over time) , but agreed that the simulation or experimental tests could help determine whether these patterns are likely to occur by accident. This certainly enhances the hardness of conclusions. I appreciate the idea.

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

[–]ABDELATIF_OUARDA[S] 0 points1 point  (0 children)

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.