Everything you need to understand a dataset
From the first upload to a downloadable executive report — here is what runs under the hood, all grounded in deterministic computation.
Automatic profiling
Row/column counts, dtype inference, semantic column mapping, and a per-column quality profile the moment you upload.
Data quality scan
Missing-value severity, duplicate detection, constant and high-cardinality columns, and IQR-based outlier flags.
Business metrics & EDA
Revenue, margins, top categories/products, trends, and seasonality — all computed deterministically in Pandas.
Trends & correlations
Period-over-period movement, volatility, anomaly points, and a Pearson correlation matrix with strong-pair detection.
Prediction & model evaluation
An optional Ridge / RandomForest model with a real held-out evaluation — R², RMSE, MAE, or accuracy and F1.
Explainable AI (SHAP)
Global feature importance and plain-English explanations so a prediction is never a black box.
Data-leakage analysis
Flags suspicious near-perfect correlations and target leakage, with financial scale-effects handled correctly.
Compact, de-duplicated report
A 1–2 page HTML/PDF with KPIs, charts, recommendations, and XAI — every fact appears exactly once.
What's inside the report
Concise by design — and free of repeated content.
Dataset snapshot
Filename, shape, detected type, and the important columns.
Key metrics
The headline KPIs — surfaced once, never repeated downstream.
Data quality
Quality score, missing values, duplicates, and the top warning.
Important insights
The highest-signal findings, ranked and de-duplicated.
Charts
Up to two auto-selected visualisations with takeaways.
Model performance evaluation
R²/RMSE/MAE or accuracy/F1 with a plain-English read and leakage note.
Recommendations
At most three concrete, prioritised next steps.
Explainable AI
SHAP feature importance — always the final section.
Works fully offline in Mock mode with no API keys — the LLM is optional and only refines wording.