Machine learning Classification model for binary input and output data I have a large longitudinal dataset with 5 minutes granularity for a period of around 30 months from thousands of households. I want to classify them as binary output (0/1) based on the input which is also a set of binary variables (sensors activated or not- 0/1. I have a training dataset available with the labeled binary output(0/1) with binary inputs.


I want to know which Machine learning model will be best for this type of case where both input and outputs are binary in nature.

Whether Logistic regression is one of the options or not?
Your problem is one of "sequence classification" for which Recurrent Neural Networks (RNN) e.g. Long short-term memory (LSTM) are generally used. 

See here for a good example: https://machinelearningmastery.com/sequence-classification-lstm-recurrent-neural-networks-python-keras/

and here for a technical paper: https://www.cs.sfu.ca/~jpei/publications/Sequence%20Classification.pdf

Here is a specialized package for sequence classification which uses convolutional neural network (CNN): https://github.com/aajanki/sequence-classification

CPT algorithm, an accurate method for sequence prediction, can also be used here: https://www.analyticsvidhya.com/blog/2018/04/guide-sequence-prediction-using-compact-prediction-tree-python/ A continous output can easily be rounded to 0 or 1 to get binary result.