ImputeViz: A Visual Analytics Dashboard for Diagnosing Missing Data and Comparing Imputation Methods
arXiv:2607.08579v1 Announce Type: cross Abstract: Missing data is a persistent obstacle in scientific, social science, and public health research, often biasing analyses and placing accountability on analysts for how they handle missing values. We introduce ImputeViz, an integrated visual analytics dashboard that supports diagnosing missingness, configuring imputation models, and evaluating results. The system brings together widely used methods, including MICE, Random Forest, XGBoost, and kNN, within an interactive environment that makes missingness patterns explicit. To support geospatial re
The proliferation of AI and data-driven research across various domains necessitates robust tools for data quality management and reliable model building, making solutions like ImputeViz timely.
Reliable handling of missing data is crucial for accurate analysis and model performance, impacting decision-making in critical fields like public health and scientific research.
The availability of integrated visual analytics dashboards simplifies the complex process of diagnosing missing data and comparing imputation methods, democratizing access to best practices.
- · Data scientists
- · Public health researchers
- · AI/ML model developers
- · Visual analytics software providers
- · Organizations with poor data quality practices
- · Manual data imputation workflows
Improved accuracy and reliability of AI models and research findings due to better data imputation.
Faster development cycles for data-intensive applications as data cleaning becomes more efficient.
Enhanced trust in AI-driven insights, particularly in sensitive areas like healthcare, as data provenance and handling become more transparent.
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