Predictive Analytics for Construction

Predictive Analytics for Construction

Overview

Use historical project data to predict future outcomes: cost overruns, schedule delays, quality issues, and risks. Apply machine learning models tailored for construction industry patterns.

Business Case

Predictive analytics enables proactive project management:

• Early Warning: Identify projects likely to overrun before it happens
• Resource Optimization: Allocate resources based on predicted needs
• Risk Mitigation: Focus on high-risk areas early
• Better Estimates: Learn from historical accuracy

Technical Implementation

from dataclasses import dataclass, field
from typing import List, Dict, Any, Optional, Tuple
import pandas as pd
import numpy as np
from datetime import datetime
from sklearn.ensemble import RandomForestRegressor, GradientBoostingClassifier
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.metrics import mean_absolute_error, accuracy_score, classification_report
import warnings
warnings.filterwarnings('ignore')

@dataclass
class PredictionResult:
    prediction: float
    confidence: float
    prediction_type: str
    features_used: List[str]
    feature_importance: Dict[str, float]
    comparable_projects: List[str]
    risk_factors: List[str]

@dataclass