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Apply machine learning for construction project risk assessment. Predict schedule delays, cost overruns, and safety incidents using historical data and project characteristics.
Passed 14 of 23 test cases with the skill, and 4 of 23 without it.
and 13 more in the full report
11 fixed3 passed either way8 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.
In our construction schedule risk pipeline, we want to train a Random Forest model on preprocessed, scaled features to classify whether a project will suffer a schedule delay. What hyperparameter values for tree depth and random seed should be passed to RandomForestClassifier?
| Checked | Without skill | With skill |
|---|---|---|
| Sets max_depth equal to 10 on the schedule classification model. | Missed | Passed |
| Sets random_state equal to 42 on the classifier instance. | Passed | Passed |
The instructions your model reads when the skill loads.
This skill implements ML-based risk assessment for construction projects. Predict potential risks before they occur and prioritize mitigation strategies based on data-driven insights.
Risk Categories:
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier, GradientBoostingRegressor
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
# Load historical project data
projects = pd.read_csv("project_history.csv")
# Features for risk prediction
features = ['project_size_m2', 'budget_usd', 'duration_days',
'complexity_score', 'team_size', 'similar_projects_exp']
X = projects[features]
y_delay = projects['had_delay'] # Binary: 1=delay, 0=on-time
# Train risk model
X_train, X_test, y_train, y_test = train_test_split(X, y_delay, test_size=0.2)
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
# Predict risk for new project
new_project = [[5000, 2000000, 365, 3, 50, 5]]
risk_probability = model.predict_proba(new_project)[0][1]
test cases passed, vs 4 / 23 without
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