Classify if someone is home based on time I have a dataset with locations and a timestamp of a subject. For each location and timestamp I determined by comparing the location to the home address if the subject was at home or not (0/1) and added this value to the dataset.

Now, I want to train a model to learn based on the timestamp when it is most likely that the subject is at home. Thus, if you give the model some timestamp, it will classify if the subject was at home at this time. The model learns the "best time" for someone being at home so to say.

Obviously people are not at home at the same time every day but over a long period of time there should be some pattern and I want the model to classify based on this pattern.

What would be a fitting algorithm to do this?
This is an ideal case for feature engineering!

I did this same case for myself using the google takeaway data to predict whether I am at home or at work.

Instead of just using time I extracted the following features:


Work Day --> 1 / 0
Day of the Week
Month
Year
Time


I then trained a random forest classification model to tell me whether I am at home, at work or other place based on those five features.

As a successive step I used this model to actually identify dates where I "moved" or was "on holiday" because of the difference between prediction and actual labels.