Predict if user will come the next day or not im novice in data science field, 
i have a problem statement: to predict which users will come online the next day. 

i have two datasets of the user: 

1st dataset of the user ( master table ) which has the overal information of the user: 

uid totlike totview totshare last_social_activity   last_service_activity   last_active_date    most_active_day   is_active( True if(last_active_day - present date )&lt; 30 )
1   200     500     115      like                   see_news_feed           11/12/2017           Monday             TRUE
2   300     600     2223     share                  see_dashboard           11/13/2017           Monday             TRUE
4   500     237     311      comment                see_news_feed           11/14/2016           Wednesday          FALSE



totlike - total like   
totview - total view    
totshare- total share


2nd data set of the user ( everyday activity ): Historical Data of user activity with time stamp.

date       uid  total_like  total_view  total_share last_social_activity    last_service_activity
11/4/2016   1     10        20         1            like                        see_news_feed
11/4/2016   2      9        25         2            share                       see_dashboard
11/4/2016   4      1        23         3            comment                     see_news_feed
11/4/2016   3      3        14         4            like                        see_dashboard
11/8/2016   1      4        14         5            comment                     see_news_feed


what is the right way to predict if the user will come tomrrow?
I think that you should stop thinking about modeling and instead start thinking about the meta datasets that you can draw from what you already have. I have also downvoted the previous answer you received because it's making the same mistake. 

One of the most consistent mistakes I see in data science (by all levels of scientists) is that they skip the data transform/wrangling stage and just go straight into modeling. That's a MASSIVE mistake and the answer you seek starts well within this stage. 

So, I want you to take a step back and think about your data. What do you need to know about a user? What are some of the features of each user? What are the features of when they visited? What day? What time? Was it the morning or the afternoon? How many days in a row was it? What is the gender of the user? And so on and so on. 

Your goal is to create a dataset that has one line per user and the same features for all users. From there, you'll be in a better position to make better decisions about modeling and anything else you want to know about your data.