cwicr-data-validator

# CWICR Data Validator ## Business Case ### Problem Statement Data quality issues cause: - Incorrect estimates - Budget overruns - Delayed projects - Rework costs ### Solution Systematic validation of CWICR data and estimate inputs to catch errors, outliers, and inconsistencies before they impact projects. ### Business Value - **Error prevention** - Catch issues early - **Data quality** - Ensure reliable estimates - **Consistency** - Standard validation rules - **Audit trail** - Document data issues ## Technical Implementation ```python import pandas as pd import numpy as np from typing import Dict, Any, List, Optional, Tuple from dataclasses import dataclass, field from enum import Enum from datetime import datetime class ValidationSeverity(Enum): """Validation issue severity.""" ERROR = "error" # Must fix WARNING = "warning" # Should review INFO = "info" # For awareness class ValidationCategory(Enum): """Validation categories.""" MISSING_DATA = "missing_data" INVALID_VALUE = "invalid_value" OUTLIER = "outlier" DUPLICATE = "duplicate" INCONSISTENT = "inconsistent" FORMAT = "format" @dataclass class ValidationIssue: """Single validation issue.""" field: str record_id: str category: ValidationCategory severity: ValidationSeverity message: str current_value: Any expected: str @dataclass class ValidationResult: """Complete validation result.""" total_records...