cost-prediction

What This Skill Does

The cost-prediction skill acts as a data-driven engine for construction project estimating. By leveraging historical project data—such as area, floor counts, complexity scores, and material quality—it trains machine learning models to forecast future expenditure. Following the methodologies outlined in 'Future: Predictions and Machine Learning' (DDC Book, Chapter 4.5), this skill transforms expert-driven estimations into algorithmic forecasts. It supports Linear Regression, K-Nearest Neighbors, and Random Forest architectures, allowing you to compare model performance to determine which best fits your specific dataset constraints. Beyond simple regression, the skill provides data preprocessing workflows to handle inflation adjustments, feature engineering, and categorical encoding, ensuring your training data is robust and ready for production-level inference.

Installation

You can install the skill via the OpenClaw terminal using the following command: clawhub install openclaw/skills/skills/datadrivenconstruction/cost-prediction Ensure you have the required data analysis libraries installed in your environment, specifically pandas, numpy, and scikit-learn.

Use Cases

This skill is designed for project managers, architectural estimators, and structural engineers. Primary use cases include:

• Preliminary budgeting for feasibility studies before detailed BOQ (Bill of Quantities) is available.
• Benchmarking historical project performance against current market inflation trends.
• Identifying the impact of building complexity and material quality on the final total cost.
• Optimizing capital allocation by running 'what-if' scenarios based on different building parameters.

Example Prompts

• 'Train a cost prediction model using historical_projects.csv, targeting total_cost with Linear Regression.'
• 'Prepare my construction dataset by encoding building types and adjusting costs for 3% annual inflation.'
• 'Given these parameters (5000 sqm, 10 floors, high complexity), what is the predicted cost based on our current trained model?'

Tips & Limitations

To achieve the best results, ensure your input dataset is cleaned of outliers before training. While Random Forest models capture non-linear relationships well, they require significantly more data points than Linear Regression to avoid overfitting. Always evaluate your model using R-squared and Mean Absolute Error (MAE) metrics to validate accuracy. Note that this skill uses historical trends as a baseline; unexpected market shocks or supply chain disruptions not represented in your dataset may lead to prediction variances.