Predict the best time of call I have a dataset including a set of customers in different cities of California, time of calling for each customer, and the status of call (True if customer answers the call and False if customer does not answer). 

I have to find an appropriate time of calling for future customers such that the probability of answering the call is high. So, what is the best strategy for this problem? Should I consider it as a classification problem which the hours (0,1,2,... 23) are the classes? Or should I consider it as a regression task which the time is a continuous variable? How can I make sure that the probability of answering the call will be high?

Any help would be appreciated. It also would be great if you refer me to similar problems.

Below is a snapshot of the data.
You might actually encounter problems if you model this as a regression problem without a suitable transformation. For instance, we know that most calls are probably answered during daytime and less during night time and early morning. A linear regression would have difficulty because the relationship is likely curvilinear, not linear. For the same reason, treating this as a classification task with logistic regression would also be problematic. 

As suggested by other respondents, reclassifying your data into time periods will help, and I'd suggest you try something like a decision tree or random forest first. 

That all said, this might be a case for simple descriptive statistics. If you plot the proportion answered calls by time of day (split by city or any other demographic), is there a clear best time?  If so, why complicate things with a model?