Using electronic mail have been being more and more popular recently. The companies and official electronic message exchange use email, despite the expansion of social networks. A user works as a manager can get up to hundreds of email a day, most of which should be in addition to also respond. It’s not just takes a lot of time to read these mails, buti t is very tiring. Therefore it is important to group emails and get prediction. It is a big advantage if we can be estimate when will the expected answer arrive. We can get interesting statistics if we combine e-mailing and data mining. In this way, users can get information about the habits and including an estimate for a given e-mail response duration.
The purpose of the thesis is to estimate the resply time from the saved suitable data, and then display the results for the user.
During my research, I studied literature, analyzed the existing solutions and possible technologies, and I have chosen the best option I considered, wich I realized the task with. Finally I evaluated the results.
I saved messages in the user database, which later I used in the RapidMiner software to build a model and then applied it. The results shows that the model can give a better estimate of the expected duration of the response, as if we would only examine the average user response time.