Prediction of Failure using Time series data

Question

I am using Python and Pandas. I am working on a predictive maintenance project where my intention is to predict the probability of a failure which will occur in a given time period, say 4-6 hours. I have preprocessed the data and reduced it to the following: The dataset has 4 attributes, Start time, end time, duration of the event(Which is the difference in start and end time) and fourth attribute being event which is a fail or not fail. (1 being Fail and 0 Being not fail) Sample data is as follows:

START_TIME      END_TIME        DURATION_MINUTES    EVENT
2/15/2018 2:32  2/15/2018 2:32  0.566666667           0
2/15/2018 2:32  2/15/2018 2:33  0.916666667           0
2/15/2018 2:33  2/15/2018 2:33  0.116666667           1
2/15/2018 2:33  2/15/2018 2:35  1.283333333           0
2/15/2018 2:35  2/15/2018 2:35  0.083333333           0
2/15/2018 2:35  2/15/2018 2:35  0.166666667           0
2/15/2018 2:35  2/15/2018 2:35  0                     0

I have about 120000 data instances. Can anybody let me know how I can visualize and predict at what probability a Failure (EVENT=1) will occur on any given day (Time frame of 4 hours)

Answer 1

neural nets and some deep learning should be the algorithmic route to go