I am attempting to build a predictive model based on the past historical data. I have details of specific machine failure based on the past year data. I have data from some months of 2016 and from 2017 January to November. I am attempting to predict if the machine fails in December. I have attached the past historical data based on the data transformation I performed. I am stuck at a point on how to build a model to identify if the machine throws up any repair or replace in next one month.
I need to build a classification model which will identify if the machine fails. I don't understand on how to convert this multiple rows to single row record for each machine. Do I need to create categorical for day and month. How would I represent each sensor data column. Essentially look at the right way of representing data so that it is easier for classification to predict for next one month.
train.csv contains the 1 month data of December for the machines which are already predicted. The target column is to predict in the month of December, if the machine requires a repair or replacement or no issue.