Schedule Forecaster for Construction

Schedule Forecaster for Construction

Overview

Predict project completion dates using machine learning models trained on historical data. Forecast delays based on current progress, weather patterns, resource availability, and project characteristics.

Business Case

Accurate schedule forecasting enables:

• Early Warning: Identify potential delays before they impact milestones
• Resource Planning: Adjust staffing based on predicted needs
• Client Communication: Provide reliable completion estimates
• Risk Management: Proactively address schedule risks

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, timedelta
from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split, TimeSeriesSplit
from sklearn.metrics import mean_absolute_error, mean_squared_error
import warnings
warnings.filterwarnings('ignore')

@dataclass
class ScheduleForecast:
    project_id: str
    forecast_date: datetime
    predicted_completion: datetime
    confidence_interval: Tuple[datetime, datetime]
    delay_probability: float
    delay_days: int
    key_risk_factors: List[str]
    recommended_actions: List[str]