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9bf96e7 2c57cb2 9bf96e7 557da7b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | # 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. |