Smart Manufacturing Machine Efficiency Prediction

Dataset Kaggle LINK

Overview

This project predicts the efficiency of smart manufacturing machines using machine learning. It includes a Flask-based web application for user interaction and a CI/CD pipeline for deployment.

Features

• Machine Learning Model: Predicts machine efficiency based on various input features.
• Flask Web App: User-friendly interface for predictions.
• CI/CD Pipeline: Automated build, test, and deployment using Jenkins, Docker, and ArgoCD.
• Kubernetes Orchestration: Manages containerized applications.

Workflow

Below is the workflow for the project:

How to Run the Project

1. Clone the Repository:

   git clone https://github.com/data-guru0/GITOPS-PROJECT-9.git
   cd GITOPS-PROJECT-9

2. Set Up the Environment:

   • Install dependencies using setup.py:

     python setup.py install

3. Run the Flask App:

   python application.py

   Access the app at http://localhost:5000.

4. Build and Deploy with Docker:

   • Build the Docker image:

     docker build -t smart-machine-efficiency .

   • Run the container:

     docker run -p 5000:5000 smart-machine-efficiency

5. CI/CD Pipeline:

   • Use the provided Jenkinsfile for automated builds and deployments.

Input Features

The model uses the following features for predictions:

• Operation Mode
• Temperature (°C)
• Vibration (Hz)
• Power Consumption (kW)
• Network Latency (ms)
• Packet Loss (%)
• Quality Control Defect Rate (%)
• Production Speed (units/hr)
• Predictive Maintenance Score
• Error Rate (%)
• Date and Time (Year, Month, Day, Hour)

Deployment

• Kubernetes: Use the provided manifests for deploying the app.
• ArgoCD: Sync the app with ArgoCD for continuous deployment.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contributing

Contributions are welcome! Please open an issue or submit a pull request.