I have been given two different arrays, which describe if a user is active on a site or not based on some features. The first array is for training. Each row of the array is a different user and the columns are different features. The first 896 columns describes the users using-patterns and the remaining 28 are lables (These 28 columns stands for 28 different time periods, they are labled 1 if the user is active in the time period, 0 otherwise.

The second array is for testing and has the same amount of columns, but here the last 28 rows are empty. My task is to predict the probability that a user will be active during each time period. I am a bit lost and are looking for (preferably quite simple) methods to solve this problem with.

I’ll throw out a couple of hints / clarifications.

  1. Your ‘test’ set is of no use to you since you have the dependent variables missing, so just ignore it to begin with.
  2. You will want to split you ‘training’ data into two sets to begin with, one for training a model and one for testing the model and computing the effectiveness of the model.
  3. Hopefully you have lots of records in the training set because you have so many independent variables. Some models are very sensitive to low sample size and high dimensionality.
  4. You will need to decide if the feature columns need to be encoded. You’ll have to pay particular attention to any categorical features because they need to be one hot encoded.
  5. You will need to choose a model or ensemble of models. You may start by trying a binary classifier. You will need to creat one for each of your timeframes. Each model should take the ~890 features and produce one result with a 0 or 1.
  6. You will need to figure out how to evaluate your models to understand how the choices you are making to tune each model are influencing the results.

This is not a comprehensive list, but hopefully it is helpful to get you started.

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