Predicting Industrial Machine Downtime
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Project Overview
An end-to-end machine learning solution that predicts equipment failures in manufacturing environments with 99% accuracy. The system analyzes real-time sensor data to prevent costly breakdowns and optimize maintenance schedules.
Key Features
- Real-time equipment monitoring dashboard
- Predictive models using Multiple ML algorithms
- Interactive data visualization
- Early warning system for potential failures
- Maintenance schedule optimization
Technical Stack
- Backend: Python
- Frontend: Streamlit
- ML Models:
- Logistic Regression
- Random Forest
- Gradient Boosting
- Data Processing: Pandas, NumPy
- Visualization: Plotly, Matplotlib