Property Maintenance Priority Baseline
This repository contains a small baseline model artifact created with
property-ops-ml, a reusable
Python toolkit for property-management analytics workflows.
Model Summary
The model is a transparent logistic-regression baseline for maintenance-priority
review. It scores work-order-like records and returns a review-support label.
This is not a universal production model. It is a reproducible example showing how
property-management teams can package a model artifact, document it, and load it
for inference.
Intended Use
Appropriate uses:
- workflow demonstration
- reproducible ML packaging
- review-support prototyping
- data-cleaning and feature-engineering practice
- baseline modeling before retraining on approved internal data
Out-of-scope uses:
- automated maintenance decisions
- tenant/resident decisions
- production dispatching without validation
- use as a substitute for property-management judgment
Training Data
The included artifact was trained on a tiny example file bundled with the
property-ops-ml GitHub repo. It is intentionally small and exists only to
demonstrate the workflow.
Teams should retrain and validate the model using approved internal work-order
labels before operational use.
Files
sample_maintenance_model.json: portable model artifactinference_example.py: first-use inference scriptsample_record.json: sample work-order-like recordrequirements.txt: minimal runtime dependencies
First Use
1pip install -r requirements.txt
2python inference_example.py
Expected output:
1{
2 "score": 0.8,
3 "label": "review",
4 "threshold": 0.5
5}
Exact score may vary if the artifact is regenerated.
Responsible Use
This model is for review support and learning. Any real deployment should include
data governance, validation on approved local data, threshold calibration, drift
monitoring, and human review.
