cost-prediction
Predict construction project costs using Machine Learning. Use Linear Regression, K-Nearest Neighbors, and Random Forest models on historical project data. Train, evaluate, and deploy cost prediction models.
npx skills add majiayu000/claude-skill-registry --skill cost-prediction --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 =
- Overview
- Core Concepts
- Quick Start
- Data Preparation
- Prepare Historical Dataset
- Feature Engineering
- Machine Learning Models
- Linear Regression
- K-Nearest Neighbors (KNN)
- Random Forest
- Gradient Boosting
- Model Evaluation
- Comprehensive Evaluation
- Compare Multiple Models
What does the cost-prediction skill do?
Predict construction project costs using Machine Learning. Use Linear Regression, K-Nearest Neighbors, and Random Forest models on historical project data. Train, evaluate, and deploy cost prediction models.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill cost-prediction --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.