predictive-maintenance-pipeline/README.md

Predictive Maintenance ML Pipeline

Automated end-to-end machine learning pipeline for engine predictive maintenance using GitHub Actions.

Quick Start

1. Prerequisites

• GitHub account
• Hugging Face account with token
• Python 3.9+

2. Setup

Add GitHub Secret:

1. Go to Repository Settings → Secrets → Actions
2. Add secret: HF_TOKEN = your Hugging Face token

Create Hugging Face Repositories:

• Dataset: SharleyK/PredictiveMaintenance
• Model: SharleyK/engine-predictive-maintenance

3. Usage

Upload your data:

# Place engine_data.csv in data/ folder

Push to GitHub:

git add .
git commit -m "Initial commit"
git push origin main

The pipeline will run automatically!

Pipeline Overview

1. Data Registration - Upload data to Hugging Face
2. EDA - Exploratory data analysis
3. Data Preparation - Clean and split data
4. Model Training - Train 6 models with MLflow
   • Decision Tree
   • Bagging
   • Random Forest
   • AdaBoost
   • Gradient Boosting
   • XGBoost
5. Model Registration - Upload best model to HF

Project Structure

predictive-maintenance-pipeline/
├── .github/workflows/pipeline.yml  # GitHub Actions workflow
├── scripts/                        # Python scripts
├── data/                          # Data files
├── models/                        # Trained models
├── outputs/                       # Results
└── requirements.txt               # Dependencies

Results

After pipeline execution:

• Data available at: https://huggingface.co/datasets/SharleyK/PredictiveMaintenance
• Model available at: https://huggingface.co/SharleyK/engine-predictive-maintenance
• MLflow experiments in mlruns/ folder

Documentation

• Full implementation guide in repository
• MLflow UI: mlflow ui --backend-store-uri file:./mlruns

Support

For issues, create a GitHub issue in this repository.