Your Autonomous Data Scientist

Gemini reads a compact profile of your data — never the raw file — and returns a structured recommendation: problem type, target column, models, metric, leakage risks. Every answer is then checked against the real dataset before anything runs.

The agent can only run models that are written out by hand in Python. If the AI suggests something that is not in the library, or is wrong for the problem type, the suggestion is rejected and the reason is shown to you.

Features that predict the answer suspiciously well are found and removed before training, not after. Splitting happens before feature engineering and selection, so the reported score is one you can defend.

A churn model can be 94% accurate and never once predict churn. The agent scores the objective separately from the model, checking absolute quality, the naive baseline, the class you actually care about, and stability across folds.

The executive summary avoids jargon entirely; the sections beneath it carry the full metric tables, the feature decisions and the limitations. Download it when you are ready.

If your objective needs clustering or forecasting, the agent explains why it will not run the standard workflow instead of producing a number that looks fine and means nothing. If it cannot tell which column you mean, it asks.