Agent skill · research science · datadrivenconstruction
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.
Why this skill is useful
Provides specific machine learning workflows and feature engineering techniques tailored for construction cost prediction that the AI wouldn't generate on its own.
What it needs
Requires python3 installed locally. About 5k tokens when loaded. Last updated 2026-02-14. 265 stars on the source repository.
What this skill does
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 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 Cross-Validation Prediction Pipeline Complete Prediction Function Save and Load Model Using with ChatGPT Quick Reference Task Code ------ ------ Split data traintestsplit(X, y, testsize=0.2) Linear Regression LinearRegression().fit(X, y) KNN KNeighborsRegressor(nneighbors=5) Random Forest RandomForestRegressor(nestimators=100) Predict model.predict(Xnew) MAE meanabsoluteerror(ytrue, ypred) R² Score r2score(ytrue, ypred) Cross-validate crossvalscore(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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills datadrivenconstruction/cost-prediction