Agentic Insights — Complete Analysis Report

Only the essential findings, evidence visuals, and actionable interpretations per analysis axis.
Dataset(s): [DATASET_NAME]
Generated:

Overall Findings

Executive snapshot

Key Findings

    Recommended Actions
      Technical Summary

        Clustering & Segmentation — Key Findings

        Distribution • Feature Importance • Quality & Robustness

        Key Findings

          k = [K] clusters Silhouette: [SILHOUETTE] Separation: [SEPARATION]
          Interpretation & Actions
          Cluster Distribution
          bar
          Feature Importance
          bar
          Clustering Quality & Robustness
          dual y-axis

          Outlier Detection — Key Findings

          Distribution • Patterns • Impact Analysis

          Key Findings

            Outliers: [RATE]% Count: [COUNT] Impact: [IMPACT]
            Interpretation & Actions
            Outliers by Column
            bar
            Distribution Statistics (Numeric Columns)
            boxplot

            Top Outliers by Column (Categorical)

            Column Top Outlier Values

            Time Series Forecasting — Key Findings

            Historical • Forecast • Confidence Intervals

            Key Findings

              Horizon: [HORIZON] Trend: [TREND] Accuracy: [ACCURACY]
              Interpretation & Actions
              Historical & Forecast
              line with intervals

              Root Cause Analysis — Key Findings

              Root Causes • Feature Importance • Correlations • Categorical Impacts

              Key Findings

                Target: [TARGET] Top driver: [TOP_DRIVER] Confidence: [CONFIDENCE]
                Interpretation & Actions
                Feature Importance
                bar
                Top Correlations
                bar
                Categorical Impacts (F-ratio)
                bar

                Technical Definitions

                F-ratio: Measures the variance between categories relative to variance within categories. Higher values indicate stronger categorical impact on the target variable. Values > 1 suggest meaningful differences between categories.
                Correlation: Measures the linear relationship strength between numeric features and the target. Range: -1 (perfect negative) to +1 (perfect positive). Values close to 0 indicate weak relationship.
                Significance: Statistical significance (p-value < 0.05) indicates the relationship is unlikely due to chance. Non-significant correlations may still be informative but require caution.
                Importance Score: Combined measure of feature impact, incorporating correlation strength, statistical significance, and categorical variance. Higher scores indicate stronger drivers of the target variable.
                Confidence Level: Assessment of reliability: High (strong evidence), Medium (moderate evidence), Low (weak evidence). Based on statistical strength and consistency of findings.