A company has a fleet of devices transmitting daily sensor 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.
The task is to build 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.
| Machine Learning Models Applied | Accuracy |
|---|---|
| Logistic Regression | 92.59% |
| Logistic Regression with Hyperparameter Tuning | 93.16% |
| K - Nearest Neighbour | 94.87% |
This project follows the MIT LICENSE.
