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Handling Incomplete Data

1 - Decision Trees for Missing Data

  • If essential data is unavailable, use fallback algorithms or flag insights as uncertain.

  • If supplementary data is missing, proceed with the core streams but highlight exclusions.

2 - Adaptive Analysis Techniques

  • Substitutions: Replace missing metrics with correlated ones (e.g., wearable activity to approximate heart rate).

  • Threshold Adjustments: Provide partial analyses or disclaimers whenever critical data is lacking.

  • Machine Learning Imputation: Estimate unknown values using historical patterns.

3 - Data Quality Indicators

  • Confidence Levels: Assign reliability scores to summarized insights.

  • User Alerts: Notify learners or educators when incomplete data might affect interpretations.

  • Visual Indicators: Color-coded or icon-based prompts on dashboards to illustrate data quality.