Analytics
Understand your dataset like never before—automated analytics for every column. Score, visualize, and compare quality metrics across all your files.
What You Get
View 20+ computed data quality metrics (e.g., Missing Values %, Class Overlap Score, Anomaly Count).
Auto-generated visualizations: bar charts, scorecards, and issue breakdowns.
Drill down into individual feature metrics.
Detect outliers, imbalance, drift, duplicates, and inconsistencies.
System-assigned <b>Data Quality Score (0–100)</b> with interpretation badges like ✅ Great, ⚠️ Medium, ❌ Poor.
Top Analytics Insights
20+ Quality Metrics in One View: Every uploaded dataset is scored across 20+ metrics like Missing_Values_Pct, Outlier_Score, and Domain_Constraint_Violations.
Data Quality Score (0–100): A composite score reflects overall dataset health using weighted metrics. It's visualized using badges (Great, Good, Average, Poor).
Visual Intelligence: Automatically generated charts help compare best vs worst metrics, class imbalance, correlation heatmaps, and anomaly trends.
Severity-Aware Highlighting: Metrics are color-coded by severity: green (≥95), orange (70–85), red (<70). Helps users prioritize issues.
Impact-Weighted Scoring: Critical metrics like Label_Noise_Rate or Data_Type_Mismatch heavily influence the quality score.
Explainable Metrics Breakdown: Each metric includes a definition, formula (e.g., Outlier_Rate = Outliers / Rows), and suggested action.
Live Comparison: Scores can be compared across datasets or snapshots (e.g., before/after cleaning) using visual diffing.
Card-Level Context Popups: Hovering on a metric card gives definitions, common causes, and remediation steps via tooltips or AI chatbot help.