How to approach data prediction problem I'm new to ML and data science. I would really like high level advice how to approach the following problem. I need to predict if an engine will fail, what I've is a sensor that give a certain value between 1 to 4 so a record might look like:

1243412312431444123123234234232423


Each record is 150 readings in length. I've a few millions of this records but in no particular order (i.e. the database doesn't contain explicit timestamp although by using some quirk database artifact I can make some records appear in order)

I've 200 different engines and a label of "failed" and "active" for each one.

So I'm looking for a predictive model that can predict (or detect) using the above data whether an engine failed or not. Any advice?

Thanks,
Eden
Are you familiar with deep learning? If yes, I would advise you to one-hot encode these records and use a 2D convolutional neural network for prediction. Since these are mere numbers and there is no way to engineer new features, I'm afraid conventional ML methods won't work. And the records have only 4 unique values, so 2D-Convolutional NN can handle it. Treat them as a picture and let the NN capture the patterns and predict the result.

Best of Luck, Eden.