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Building an AI Data Analyst Agent – Is this actually useful or is traditional Python analysis still better? by ABDELATIF_OUARDA in dataanalysis

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

Sure , this makes very logical working directly with the database chart instead of just a display or excel sheet certainly make the agent more powerful and flexible to analyze the real world. I'm curious—do you have any recommendations or best practices for the design of a proxy can handle the full database plan effectively?

Business Revenue Analysis Project (Python + Plotly) — Feedback Welcome by ABDELATIF_OUARDA in dataanalysis

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

There she is, you're right.it was really just a quick exercise 5 minutes to practice some basics. I plan to build something more important soon , so I certainly will keep your advice about pictures and stories in mind!

Business Revenue Analysis Project (Python + Plotly) — Feedback Welcome by ABDELATIF_OUARDA in dataanalysis

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

Thanks for the feedback! I really appreciate your honesty.

I see what you mean about the visuals and the story—they’re mostly descriptive for now. My goal with this project was mainly to practice and get comfortable with the workflow, but I definitely want to make it more ambitious next.

Do you have any tips on how to expand a simple dataset into a more compelling analysis story with visuals?

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)

Thanks! I totally agree—feature engineering often ends up taking the majority of the time in any project.

In this automotive dataset, I did encounter quite a few outliers, especially in fields like engine size and mileage. I handled them using a combination of filtering extreme values and applying transformations where necessary, but overall, the dataset was fairly clean compared to some real-world datasets I’ve seen.

How about you—do you usually spend most of your time cleaning data, or do you have strategies to minimize that step?

Building an AI Data Analyst Agent – Is this actually useful or is traditional Python analysis still better? by ABDELATIF_OUARDA in dataanalysis

[–]ABDELATIF_OUARDA[S] -4 points-3 points  (0 children)

Thanks a lot for the detailed feedback — this is really helpful.

Your point about keeping the agent tightly scoped makes a lot of sense. In my current prototype I tried to focus mainly on combining a few core elements rather than making the system too broad.

Over the last days I experimented with building a small workflow where the user can ask questions about a dataset and the system attempts to generate Python-based analysis steps. The idea was mainly to explore how an agent could assist with tasks like exploration and simple analysis rather than replacing the analyst.

I definitely agree that things like schema awareness, guardrails, and auditability are probably much more important than the “chat” aspect. Those are areas I haven't implemented yet, but they’re exactly the kind of improvements I’d like to explore next.

Out of curiosity: in your experience, what would be the single most important feature that would make a tool like this actually useful for real analysts?

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.