[MEDIUM] Implement Data Quality Scoring System

[frankbria]

🎯 Overview

Build comprehensive data quality scoring system that evaluates dataset readiness for ML.

✅ Acceptance Criteria

• Overall quality score (0-100)
• Component scores:
  • Completeness (missing values)
  • Validity (data types, ranges)
  • Consistency (formats, patterns)
  • Uniqueness (duplicates)
  • Accuracy (outliers, anomalies)
• Quality improvement recommendations
• Quality trend tracking over time
• Quality gates for workflow progression
• Detailed quality report generation

🏗️ Technical Requirements

• Backend: QualityScoringService
• Statistical quality metrics calculation
• Scoring algorithm combining all dimensions
• Frontend: Quality score dashboard
• Integration with data profiling (#existing)

🏷️ Labels

`medium-priority`, `backend`, `frontend`, `stage-2-profiling`, `data-quality`

⏱️ Estimated Effort

2-3 weeks

[traycerai]

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🔗 Similar Issues

Related Issues

• [BLOCKER] Implement AI-Powered Data Issue Detection and Suggested Fixes #66
• [BLOCKER] Implement Comprehensive Model Evaluation Dashboard with Performance Metrics #79
• [HIGH] Implement Prediction Confidence Scores and Explanations #83
• [BLOCKER] Implement Error Analysis with Pattern Detection #81
• [BLOCKER] Implement Visual Data Cleaning Interface for Stage 3: Data Preparation #65

👤 Suggested Assignees

• frankbria

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