Methodology

1 · The analytics workflow

Every dataset moves through the same transparent pipeline so results are reproducible and auditable: understand → audit → clean → explore → model → explain → forecast → simulate → report. You can branch or export at any step; nothing is a black box.

2 · Dataset understanding

On load, ATARS profiles every column — type, cardinality, missingness, distribution — and resolves common semantic aliases (e.g. rev→revenue, qty→quantity). It infers the likely problem type (classification, regression, forecasting or clustering), candidate target and feature columns, and recommends a workflow and models.

3 · Data quality & cleaning

A quality auditor produces a readiness score from completeness, consistency, duplication, outliers and class balance, and flags potential data leakage and bias. Cleaning operations — imputation, de-duplication, outlier handling, type conversion, encoding and scaling — are applied transparently and recorded in an audit log so every transformation is traceable.

4 · Statistical analysis

Hypothesis testing and inference use standard, peer-reviewed methods from statsmodels and scipy: t-tests, z-tests, ANOVA, chi-square, correlation and normality tests, non-parametric tests (e.g. Mann-Whitney, Kruskal-Wallis), variance analysis, confidence intervals and assumption diagnostics. Test choice and assumptions are surfaced alongside results, and when you combine datasets ATARS checks whether branches differ significantly (ANOVA, Kruskal-Wallis and pairwise t-tests) rather than by chance.

5 · Machine learning & AutoML

Models cover regression, classification and clustering, built with scikit-learn — Random Forest, Gradient Boosting, SVM and linear families among them. ATARS uses train/validation splits and leakage-safe cross-validation, reports the appropriate metrics (R², MAE, RMSE for regression; accuracy, precision, recall, F1 for classification), and the AutoML engine trains several candidates, tunes them and ranks a leaderboard to select the best — with a Fast mode that samples large data for a quick ranking and a Full-accuracy mode that trains on every row, plus a live progress view. Trained models support single and batch prediction.

6 · Explainability

Predictions are explained with SHAP (SHapley Additive exPlanations), giving both global feature importance and local, per-prediction contribution breakdowns. This is what lets you see why a model produced a result, not just what it produced.

7 · Forecasting

Time-series forecasting uses classical, well-understood methods (e.g. ARIMA and Holt-Winters exponential smoothing) to capture trend and seasonality, and reports confidence intervals so uncertainty is explicit rather than hidden.

8 · Querying, SQL & combining

SQL Lab runs full SQL against your data with DuckDB — joins, window functions, CTEs and aggregations — so you can reshape, filter and aggregate exactly how you want, then promote a query result back into the working dataset. Multiple files can be combined by stacking rows or joining on a detected key, and every branch can be compared side by side.

9 · Business analytics

Beyond generic charts, ATARS includes the metrics teams report on: cohort & retention analysis, RFM (Recency, Frequency, Monetary) customer segmentation, and time-series decomposition into trend, seasonality and residual components — each computed transparently from your own columns.

10 · Assumptions & limits

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