Predictive Maintenance

Project Overview

This project focuses on building a classification model to predict potential equipment failures before they occur. We'll tackle the common challenge of imbalanced data in maintenance scenarios and learn how to build effective predictive models.

Key Learning Objectives

• Handling imbalanced datasets
• Feature engineering for time-series maintenance data
• Implementing classification algorithms
• Model evaluation with appropriate metrics
• Creating actionable maintenance recommendations

Technologies Used

• Python
• scikit-learn
• imbalanced-learn for handling class imbalance
• XGBoost for classification
• pandas for data manipulation

Additional Resources

For detailed implementation guide, code examples, and step-by-step instructions, please visit the GitHub Project Repository.