Construction Cost Prediction with Machine Learning

Overview

Based on DDC methodology (Chapter 4.5), this skill enables predicting construction project costs using historical data and machine learning algorithms. The approach transforms traditional expert-based estimation into data-driven prediction.

Book Reference: "Будущее: прогнозы и машинное обучение" / "Future: Predictions and Machine Learning"

> "Предсказания и прогнозы на основе исторических данных позволяют компаниям принимать более точные решения о стоимости и сроках проектов." > — DDC Book, Chapter 4.5

Core Concepts

Historical Data → Feature Engineering → ML Model → Cost Prediction
    │                    │                │              │
    ▼                    ▼                ▼              ▼
Past projects      Prepare data      Train model    New project
with costs         for ML            on history     cost forecast

Quick Start

python
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, r2_score

# Load historical project data
df = pd.read_csv("historical_projects.csv")

# Features and target
X = df[['area_m2', 'floors', 'complexity_score']]
y = df['total_cost']

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Train model
model = LinearRegression()
model.fit(X_train, y_train)

# Predict
predictions = model.predict(X_test)
print(f"R² Score: {r2_score(y_test, predictions):.2f}")
print(f"MAE: ${mean_absolute_error(y_test, predictions):,.0f}")

# Predict new project
new_project = [[5000, 10, 3]]  # area, floors, complexity
cost = model.predict(new_project)
print(f"Predicted cost: ${cost[0]:,.0f}")

Data Preparation

Prepare Historical Dataset

python
import pandas as pd
import numpy as np

def prepare_cost_dataset(df):
    """Prepare historical project data for ML"""
    # Select relevant features
    features = [
        'area_m2',
        'floors',
        'building_type',
        'location',
        'year_completed',
        'complexity_score',
        'material_quality',
        'total_cost'
    ]

    df = df[features].copy()

    # Handle missing values
    df = df.dropna(subset=['total_cost'])
    df['complexity_score'] = df['complexity_score'].fillna(df['complexity_score'].median())

    # Encode categorical variables
    df = pd.get_dummies(df, columns=['building_type', 'location'])

    # Calculate derived features
    df['cost_per_m2'] = df['total_cost'] / df['area_m2']
    df['cost_per_floor'] = df['total_cost'] / df['floors']

    # Adjust for inflation (to current year prices)
    current_year = 2024
    inflation_rate = 0.03  # 3% annual
    df['years_ago'] = current_year - df['year_completed']
    df['adjusted_cost'] = df['total_cost'] * (1 + inflation_rate) ** df['years_ago']

    return df

# Usage
df = pd.read_csv("projects_history.csv")
df_prepared = prepare_cost_dataset(df)

Feature Engineering

python
def engineer_features(df):
    """Create additional features for better predictions"""
    # Interaction features
    df['area_x_floors'] = df['area_m2'] * df['floors']
    df['area_x_complexity'] = df['area_m2'] * df['complexity_score']

    # Polynomial features
    df['area_squared'] = df['area_m2'] ** 2

    # Log transforms (for skewed features)
    df['log_area'] = np.log1p(df['area_m2'])

    # Binned features
    df['size_category'] = pd.cut(
        df['area_m2'],
        bins=[0, 1000, 5000, 10000, float('inf')],
        labels=['small', 'medium', 'large', 'xlarge']
    )

    return df

Machine Learning Models

Linear Regression

python
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

def train_linear_model(X_train, y_train):
    """Train Linear Regression model with scaling"""
    pipeline = Pipeline([
        ('scaler', StandardScaler()),
        ('regressor', LinearRegression())
    ])

    pipeline.fit(X_train, y_train)

    # Feature importance (coefficients)
    coefficients = pd.DataFrame({
        'feature': X_train.columns,
        'coefficient': pipeline.named_steps['regressor'].coef_
    }).sort_values('coefficient', key=abs, ascending=False)

    return pipeline, coefficients

# Usage
model, importance = train_linear_model(X_train, y_train)
print("Feature Importance:")
print(importance)

K-Nearest Neighbors (KNN)

python
from sklearn.neighbors import KNeighborsRegressor
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import GridSearchCV

def train_knn_model(X_train, y_train):
    """Train KNN model with optimal k"""
    # Scale features
    scaler = StandardScaler()
    X_scaled = scaler.fit_transform(X_train)

    # Find optimal k using cross-validation
    param_grid = {'n_neighbors': range(3, 20)}
    knn = KNeighborsRegressor()
    grid_search = GridSearchCV(knn, param_grid, cv=5, scoring='neg_mean_absolute_error')
    grid_search.fit(X_scaled, y_train)

    print(f"Best k: {grid_search.best_params_['n_neighbors']}")
    print(f"Best MAE: ${-grid_search.best_score_:,.0f}")

    return grid_search.best_estimator_, scaler

# Usage
knn_model, scaler = train_knn_model(X_train, y_train)

Random Forest

python
from sklearn.ensemble import RandomForestRegressor

def train_random_forest(X_train, y_train):
    """Train Random Forest model"""
    rf = RandomForestRegressor(
        n_estimators=100,
        max_depth=10,
        min_samples_split=5,
        random_state=42
    )

    rf.fit(X_train, y_train)

    # Feature importance
    importance = pd.DataFrame({
        'feature': X_train.columns,
        'importance': rf.feature_importances_
    }).sort_values('importance', ascending=False)

    return rf, importance

# Usage
rf_model, importance = train_random_forest(X_train, y_train)
print("Feature Importance:")
print(importance.head(10))

Gradient Boosting

python
from sklearn.ensemble import GradientBoostingRegressor

def train_gradient_boosting(X_train, y_train):
    """Train Gradient Boosting model"""
    gb = GradientBoostingRegressor(
        n_estimators=200,
        learning_rate=0.1,
        max_depth=5,
        random_state=42
    )

    gb.fit(X_train, y_train)
    return gb

# Usage
gb_model = train_gradient_boosting(X_train, y_train)

Model Evaluation

Comprehensive Evaluation

python
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import numpy as np

def evaluate_model(model, X_test, y_test, model_name="Model"):
    """Comprehensive model evaluation"""
    predictions = model.predict(X_test)

    metrics = {
        'MAE': mean_absolute_error(y_test, predictions),
        'RMSE': np.sqrt(mean_squared_error(y_test, predictions)),
        'R²': r2_score(y_test, predictions),
        'MAPE': np.mean(np.abs((y_test - predictions) / y_test)) * 100
    }

    print(f"\n{model_name} Evaluation:")
    print(f"  MAE:  ${metrics['MAE']:,.0f}")
    print(f"  RMSE: ${metrics['RMSE']:,.0f}")
    print(f"  R²:   {metrics['R²']:.3f}")
    print(f"  MAPE: {metrics['MAPE']:.1f}%")

    return metrics, predictions

# Usage
metrics, predictions = evaluate_model(model, X_test, y_test, "Linear Regression")

Compare Multiple Models

python
def compare_models(models, X_test, y_test):
    """Compare multiple models"""
    results = []

    for name, model in models.items():
        metrics, _ = evaluate_model(model, X_test, y_test, name)
        metrics['Model'] = name
        results.append(metrics)

    comparison = pd.DataFrame(results)
    comparison = comparison.set_index('Model')

    print("\nModel Comparison:")
    print(comparison.round(2))

    return comparison

# Usage
models = {
    'Linear Regression': linear_model,
    'KNN': knn_model,
    'Random Forest': rf_model,
    'Gradient Boosting': gb_model
}
comparison = compare_models(models, X_test, y_test)

Cross-Validation

python
from sklearn.model_selection import cross_val_score

def cross_validate_model(model, X, y, cv=5):
    """Perform cross-validation"""
    scores = cross_val_score(model, X, y, cv=cv, scoring='neg_mean_absolute_error')
    mae_scores = -scores

    print(f"Cross-Validation MAE: ${mae_scores.mean():,.0f} (+/- ${mae_scores.std():,.0f})")
    return mae_scores

# Usage
cv_scores = cross_validate_model(rf_model, X, y)

Prediction Pipeline

Complete Prediction Function

python
import joblib

def create_prediction_pipeline(model, feature_names, scaler=None):
    """Create a reusable prediction pipeline"""

    def predict_cost(project_data):
        """
        Predict cost for new project

        Args:
            project_data: dict with project features

        Returns:
            Predicted cost and confidence interval
        """
        # Create DataFrame from input
        df = pd.DataFrame([project_data])

        # Ensure all required features
        for col in feature_names:
            if col not in df.columns:
                df[col] = 0

        df = df[feature_names]

        # Scale if necessary
        if scaler:
            df = scaler.transform(df)

        # Predict
        prediction = model.predict(df)[0]

        # Confidence interval (simple estimation)
        confidence = 0.15  # 15% margin
        lower = prediction * (1 - confidence)
        upper = prediction * (1 + confidence)

        return {
            'predicted_cost': prediction,
            'lower_bound': lower,
            'upper_bound': upper,
            'confidence_level': f"{(1-confidence)*100:.0f}%"
        }

    return predict_cost

# Usage
predictor = create_prediction_pipeline(rf_model, X.columns.tolist())

# Predict new project
new_project = {
    'area_m2': 5000,
    'floors': 8,
    'complexity_score': 3,
    'material_quality': 2
}

result = predictor(new_project)
print(f"Predicted Cost: ${result['predicted_cost']:,.0f}")
print(f"Range: ${result['lower_bound']:,.0f} - ${result['upper_bound']:,.0f}")

Save and Load Model

python
import joblib

# Save model
def save_model(model, filepath):
    """Save trained model to file"""
    joblib.dump(model, filepath)
    print(f"Model saved to {filepath}")

# Load model
def load_model(filepath):
    """Load model from file"""
    model = joblib.load(filepath)
    print(f"Model loaded from {filepath}")
    return model

# Usage
save_model(rf_model, "cost_prediction_model.pkl")
loaded_model = load_model("cost_prediction_model.pkl")

Using with ChatGPT

python
# Prompt for ChatGPT to help with cost prediction

prompt = """
I have historical construction project data with these columns:
- area_m2: Building area in square meters
- floors: Number of floors
- building_type: residential, commercial, industrial
- total_cost: Total project cost in USD

Write Python code using scikit-learn to:
1. Prepare the data for machine learning
2. Train a Random Forest model
3. Evaluate the model
4. Predict cost for a new 3000 m² commercial building with 5 floors
"""

Quick Reference

| Task | Code | |------|------| | Split data | train_test_split(X, y, test_size=0.2) | | Linear Regression | LinearRegression().fit(X, y) | | KNN | KNeighborsRegressor(n_neighbors=5) | | Random Forest | RandomForestRegressor(n_estimators=100) | | Predict | model.predict(X_new) | | MAE | mean_absolute_error(y_true, y_pred) | | R² Score | r2_score(y_true, y_pred) | | Cross-validate | cross_val_score(model, X, y, cv=5) | | Save model | joblib.dump(model, 'file.pkl') |

Best Practices

1. Data Quality: More historical data = better predictions
2. Feature Selection: Include relevant project characteristics
3. Inflation Adjustment: Normalize costs to current prices
4. Regular Retraining: Update model with new completed projects
5. Ensemble Methods: Combine multiple models for robustness
6. Confidence Intervals: Always provide prediction ranges

Resources

• Book: "Data-Driven Construction" by Artem Boiko, Chapter 4.5
• Website: https://datadrivenconstruction.io
• scikit-learn: https://scikit-learn.org

Next Steps

• See duration-prediction for project duration forecasting
• See ml-model-builder for custom ML workflows
• See kpi-dashboard for visualization
• See big-data-analysis for large dataset processing