cost-prediction

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

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

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)