predictive-maintenance-random-forest

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.