0
$\begingroup$

I am very new in data science and machine learning. I need to find out/predict a time in which the user is active in a website during the day. I have a dataset with 3 columns listed as "user_id", "login_time" and "logout_time". Now I am trying to make another column "active_time" in which I'm trying to compute the user's active time in the website by subtracting the login time from logout time and it can be multiple as user can access website multiple times in a day. Now I need to predict the time in which the user is active in the website where active time is the target variable and login, logout time as predictor. I also trying to make a linear regression model for this prediction. But I don't know whether my process is correct or not for this problem. Can anyone please let me know which type of model I need to build for this prediction? Is it will be Linear regression, logistic regression or time series ? And how can I do this ?

$\endgroup$
  • $\begingroup$ If you have login and logout times as predictors, you don't need to build a model, as the active time = the logout time - the login time; a perfect prediction, so to speak. $\endgroup$ – jbowman Nov 13 '18 at 3:41
  • $\begingroup$ I can calculate the active time. But with the analysis of active time in last few months how can I predict the time in which the user will be active tomorrow? $\endgroup$ – Nuibb Nov 13 '18 at 5:02
  • $\begingroup$ "Now I need to predict the time in which the user is active in the website where active time is the target variable and login, logout time as predictor." If you don't have the login, logout times, you can't use them as predictors, which leaves you with nothing but their histories of active times, which is an interesting, but different from regression, problem. $\endgroup$ – jbowman Nov 13 '18 at 5:11

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.