A company has a fleet of devices transmitting daily telemetry readings. They would like to create a predictive maintenance solution to proactively identify when maintenance should be performed. This approach promises cost savings over routine or time-based preventive maintenance, because tasks are performed only when warranted.
You are tasked with building a predictive model using machine learning to predict the probability of a device failure. When building this model, be sure to minimize false positives and false negatives. The column you are trying to predict is called failure with binary value 0 for non-failure and 1 for failure.
** Important Highlights
- VIF
- Resampling Using Pandas
- LabelEncoder
- Bagging and Boosting
- LGBMClassifier