---
language:
  - en
license: apache-2.0
tags:
  - tabular-classification
  - gradient-boosting
  - predictive-maintenance
  - scikit-learn
library_name: sklearn
datasets:
  - engine-sensor-data  # Replace with your actual dataset name if known
metrics:
  - recall
  - roc_auc
  - pr_auc
---

# Predictive Maintenance – Gradient Boosting Model

## Model Overview
This model is a recall-optimized Gradient Boosting classifier developed to support predictive maintenance for engine systems. The primary objective is to identify engines likely to require maintenance before failure occurs.

## Training Data
The model was trained on a prepared engine sensor dataset sourced from the Hugging Face Dataset Hub. The dataset contains structured numeric sensor readings representing engine operating conditions.

## Objective
- Minimize missed engine failures (false negatives)
- Prioritize recall for the faulty engine class

## Evaluation Metrics
- Recall (Faulty): ~0.84
- ROC-AUC: ~0.70
- PR-AUC: ~0.80

## Intended Use
This model is intended for:
- Predictive maintenance decision support
- Risk-based maintenance scheduling
- Offline or batch inference scenarios

## Limitations
- Trained on a static, pre-processed dataset
- Does not incorporate temporal or sequential dependencies
- Threshold selection may require calibration based on operational risk tolerance

## Model Artifacts
The repository contains a serialized `joblib` model file that can be loaded directly for inference in Python-based environments.