Predictive Maintenance – Tuned Random Forest Model
Model Description
This model is a tuned Random Forest classifier trained to predict engine maintenance requirements using sensor data such as RPM, oil pressure, fuel pressure, and temperature readings.
Training Data
- Dataset: Predictive Maintenance Engine Sensor Dataset
- Source: Hugging Face Dataset Hub (
manjuprasads/predictive-maintenance-engine-data) - Target Variable:
engine_condition(0 = Normal, 1 = Maintenance Required)
Model Objective
The model prioritizes recall for engines requiring maintenance to minimize the risk of missed failures in safety-critical environments.
Intended Use
- Early detection of engine maintenance needs
- Integration into real-time monitoring and alerting systems
Limitations
- The model is trained on snapshot sensor data and does not capture temporal trends.
- Performance may vary across unseen engine types or operating regimes.
Framework
- scikit-learn
Automated ML Pipeline
This repository includes an automated machine learning pipeline that supports:
- Data ingestion from Hugging Face dataset space
- Preprocessing and feature preparation
- Model training and evaluation
- Model artifact registration
The pipeline is implemented in a modular manner and is automation-ready.
It can be triggered via CI/CD workflows (e.g., GitHub Actions) based on code or data changes.