Predicting Machines Remaining Life And Break Down

**Problem Description**: Predictive maintenance problem can be vary in different ways like failure prediction, failure detection and predicting the remaining life time of a machine. This model mainly focuses on when in-service machine will likely to fail, so that future maintenance can be planned accordingly. Not only the former one, but this model can also predict when the machine will be likely to fail. A major problem faced in manufacturing or mining industries is significant costs which are associated with the down time of the machinery. So most of these businesses are interested in predicting the problems, which are responsible for down time. The business problem in our example is to predict the remaining life time of a machine and detecting possibility of a machine to fail within a window period. This problem is formatted into regression for predicting remaining useful life and binary classification for detecting the machine failure status. **Inputs**: The input data uses machinery maintenance dataset from kaggle website and you can download it from there. The dataset contains 1000 observations and 7 columns, for binary classification the label column is ‘broken’ and for regression problem the label column is ‘RUL’ which has been obtained by doing feature engineering other columns in the dataset. Noise in the distribution of data may accounts in reducing the goodness of model, so we need to take care of it. Rolling mean and rolling sd comes to recue to deal with this problem. **Output**: The training data has two outputs, one is binary variable showing weather the machine has break down or not. Another one is a regression variable that has been created by doing feature engineering which gives the remaining life of the machine.