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Engine Predictive Maintenance

Title Page – Predictive Maintenance for Engine Condition Classification

Overview

This project implements an end-to-end MLOps pipeline for engine predictive maintenance: classify engine condition (Normal vs Maintenance Required) from sensor readings (RPM, lubricating oil pressure, fuel pressure, coolant pressure, oil and coolant temperatures). The pipeline covers data registration, exploratory data analysis, data preparation, model building with experimentation tracking (MLflow), model deployment (Streamlit on Hugging Face Spaces), and automated workflows (GitHub Actions).

Repository structure

Path Description
Engine_PM_Interim_Notebook_final.ipynb Main notebook: EDA, prep, training, deployment, and report sections
engine_pm_project/data/ Raw data folder (e.g. engine_data.csv)
engine_pm_project/model_building/ Scripts: data_register.py, prep.py, train.py
engine_pm_project/deployment/ Dockerfile, Streamlit app.py, requirements.txt, deploy_to_hf_spaces.py
.github/workflows/pipeline.yml CI: register-dataset → data-prep → model-training → deploy-hosting

Links

Resource Link
GitHub repository github.com/ananttripathi/engine-pm-project
Hugging Face Space (Streamlit app) huggingface.co/spaces/ananttripathiak/engine-pm-streamlit
Hugging Face dataset huggingface.co/datasets/ananttripathiak/engine-pm-data
Hugging Face model huggingface.co/ananttripathiak/engine-pm-model

Running the notebook

cd /path/to/Interim_Submission
jupyter nbconvert --to notebook --execute --inplace Engine_PM_Interim_Notebook_final.ipynb

Place title_page.png in the same directory as the notebook so the image displays correctly.

Deployment

  • Streamlit app: The Space runs the app in engine_pm_project/deployment/ (Docker). It loads the model from the Hugging Face model hub and predicts from six sensor inputs.
  • CI: Push to main runs the pipeline; set HF_TOKEN in GitHub Secrets for data/model/Space uploads.

License

See repository for license information.