Predictive Analytics for Construction — forecast overruns, delays, and risks
Forecast project outcomes from historical data: cost overruns, schedule delays, and risk probabilities — Gradient Boosting models, similar-project search, and a generated prediction report with key risk factors
- What
- Forecast project outcomes from historical data: cost overruns, schedule delays, and risk probabilities — Gradient Boosting models, similar-project search, and a generated prediction report with key risk factors
- Cost
- Free
- Needs
- Python 3 with pandas, numpy, and scikit-learn — historical project data (original estimates, final costs, planned/actual durations, change orders, complexity scores) to train anything real
- Install
- Copy the installer prompt below into your Muse — your agent does the rest.
Curated by Skill Harbor — @datadrivenconstruction's predictive-analytics-construction skill, listed here with credit to its creator (companion to the "Data-Driven Construction" book, chapter 4.1): turn historical project data into forward-looking risk intelligence. It ships a complete `ConstructionPredictiveAnalytics` Python class — train a cost-overrun model (GradientBoostingRegressor on estimate vs final cost with feature importance), a schedule-delay classifier (GradientBoostingClassifier on planned vs actual duration), query predictions for new projects with confidence and ranked risk factors, find similar historical projects via nearest-neighbors for comparable outcomes, and generate a full markdown prediction report. Honest caveats: needs real historical data with the expected columns (estimates, final costs, durations, change orders, complexity scores) — the code is a framework, not a pretrained model; the confidence values are simplified approximations, not calibrated probabilities; pair it with the sibling cost-prediction skill for deeper model comparison. MIT licensed. Skill Harbor never reviews the code, review it yourself before use. Discovered via skills.sh.
Version:
Install
Prerequisites: Python 3 with pandas, numpy, and scikit-learn — historical project data (original estimates, final costs, planned/actual durations, change orders, complexity scores) to train anything real Install "Predictive Analytics for Construction — forecast overruns, delays, and risks" for me. It gives my agent @datadrivenconstruction's predictive analytics workflow: train a Gradient Boosting cost-overrun model and a Gradient Boosting schedule-delay classifier on historical data, predict overruns and delay probabilities for new projects with ranked risk factors, find similar past projects via nearest-neighbors, and generate a full markdown prediction report. MIT licensed. Repository: https://github.com/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/blob/main/2_DDC_Book/4.1-Analytics-KPI-Dashboard/predictive-analytics-construction/SKILL.md 1. Fetch the SKILL.md file (and any helper files) from the repository path into a temporary folder and summarize what it does in one or two sentences. 2. Safety check: review the SKILL.md and scripts for anything suspicious (unexpected network calls, shell commands, credential harvesting). This repo should contain zero secrets in code. Verify that holds here; STOP on any red flag and tell me. 3. Install it as a skill: copy SKILL.md and its helper files into the agent's skills directory, in a folder named "predictive-analytics-construction". 4. Verify with no network calls: frontmatter valid, files in place. 5. Report what was installed, where, and what I still need to do myself (e.g. pip install pandas numpy scikit-learn; prepare historical data with the expected columns; treat confidence values as rough guides, not calibrated probabilities). GitHub is optional: if I have a GitHub account or the gh CLI, you may use it; otherwise public access is fine. Never require it unless it's in the prerequisites above. Rules: don't touch anything outside the temp folder and the install target. If anything looks off, stop and ask me.
Questions
How do I install a build?
Every product page includes a copy-paste install prompt. Paste it into your Muse and it sets the build up for you — no manual configuration.
Where does my money go?
Straight to the seller. Skill Harbor never processes payments: checkout happens on the seller’s own page, usually Stripe.
What does the ✓ next to a creator’s name mean?
It means we confirmed the identity of the person behind the listing. It says nothing about the code itself — always check a build before installing it.