Predicting churn - deal with missing dates in time series and improve modelling result
It would be interested to deal with it as a sequence classification problem. For instance, you could use HMM (Hidden Markov Model) or equivalent to classify the sequences. The data format would be:

ID:  sequence      label
id1: 39,0,6,...,88  1
id2: 300, 2         1
id3: 1              1
id4: 100            0


Some suggestions:


Create more samples to balance also your dataset (e.g id1   39, 0    0)
Possibly bin the variables (e.g. to the decimal 6 -&gt; 10, 39 -&gt; 40)