# 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.