This project is a web-based application designed for predictive maintenance in industrial settings. It leverages advanced machine learning models to predict equipment failures before they occur, minimizing downtime and extending equipment lifespan.
- Web Interface: Built with Streamlit, offering a user-friendly interface for navigation across various insightful pages.
- Real-time Predictions: Allows users to input process parameters and receive predictions on potential equipment failures.
- Data Visualization: Users can visualize the data and analyze the behavior of the equipment through interactive charts and graphs.
- Performance Metrics: Includes a comparison of different machine learning models based on precision, recall, and F1 score.
Explore the application in action through our live demo. Visit Web App Demo to see how you can interact with the application and utilize its features for predictive analysis.