data-anomaly-detector

# Data Anomaly Detector for Construction

## Overview

Detect unusual patterns, outliers, and anomalies in construction data. Identify cost overruns, schedule delays, productivity issues, and data quality problems before they impact projects.

## Business Case

Construction data often contains anomalies that indicate:
- Cost estimate errors or fraud
- Schedule logic issues
- Productivity problems
- Data entry mistakes
- Equipment or material issues

Early detection prevents costly corrections and project delays.

## Technical Implementation

```python
from dataclasses import dataclass, field
from typing import List, Dict, Any, Optional, Tuple
from enum import Enum
import pandas as pd
import numpy as np
from datetime import datetime
from scipy import stats

class AnomalyType(Enum):
    OUTLIER = "outlier"
    PATTERN_BREAK = "pattern_break"
    MISSING_SEQUENCE = "missing_sequence"
    DUPLICATE = "duplicate"
    IMPOSSIBLE_VALUE = "impossible_value"
    TREND_DEVIATION = "trend_deviation"

class AnomalySeverity(Enum):
    CRITICAL = "critical"
    HIGH = "high"
    MEDIUM = "medium"
    LOW = "low"

@dataclass
class Anomaly:
    id: str
    anomaly_type: AnomalyType
    severity: AnomalySeverity
    field: str
    value: Any
    expected_range: Optional[Tuple[float, float]] = None
    description: str = ""
    row_index: Optional[int] = None
    detection_method: str = ""
    confidence: float = 0.0
    suggested_action: str = ""

@dataclass
class AnomalyReport:
    source: str
    detected_at: datetime
    total_records: int
    anomalies: List[Anomaly]
    summary: Dict[str, int]

class ConstructionAnomalyDetector:
    """Detect anomalies in construction data."""

    # Construction-specific thresholds
    COST_THRESHOLDS = {
        'concrete_per_cy': (200, 800),
        'steel_per_ton': (1500, 4000),
        'labor_per_hour': (25, 150),
        'overhead_percentage': (5, 25),
        'conti...