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

```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 hi...