README.md

metadata

title: Predictive Maintenance for Turbofan Engines
emoji: ✈️
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 4.25.0
app_file: app.py
pinned: false

Predictive Maintenance for Turbofan Engines

A complete MLOps project demonstrating an end-to-end workflow for a predictive maintenance solution. This application uses a machine learning model to predict the Remaining Useful Life (RUL) of a turbofan engine based on operational settings and sensor data.

The project is developed within a containerized GitHub Codespaces environment and features a CI/CD pipeline that automatically trains the model and deploys the application to this Hugging Face Space.

✨ Features

• Interactive Demo: A user-friendly Gradio web interface to get real-time RUL predictions.
• Automated CI/CD: The model is automatically retrained and the application is redeployed on every push to the main branch using GitHub Actions.
• Reproducible Environment: A defined development environment using Codespaces ensures that the project can be run consistently by anyone.
• Extensible Framework: While this demo uses a turbofan engine dataset, the principles can be customized for any machinery that relies on sensor data to predict performance or potential faults.

🛠️ Technology Stack

• Backend: Python
• ML Model: Scikit-learn (Linear Regression)
• Web App: Gradio
• Dev Environment: GitHub Codespaces (Docker)
• CI/CD & Hosting: GitHub Actions, Hugging Face Spaces

🚀 How to Run Locally

To run this project on your own machine or Codespace, follow these steps.

Prerequisites

• Python 3.9 or higher
• Git

1. Clone the Repository

git clone [https://github.com/ashandilgith/predictivemaintenance-.git](https://github.com/ashandilgith/predictivemaintenance-.git)
cd predictivemaintenance-