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In this study, we intended to recommend possible books to be checked out from Seattle Public Library (SPL) within a month. While it seems possible to make daily estimates from existing data, it will be more useful to make monthly data forecasts since SPL provides fresh data every month. The information obtained here will contribute to logistic modelling. Since some assumptions on the location of the books and storage capacity of each location are required for the better management of the resources, this problem can be considered as a warehouse resource allocation problem. In order to perform the required predictions, Naïve Bayes (NB) algorithm is applied on the Seattle Public Library Dataset (SPLD), which contains all of the checkout records between 2005 and 2017 in the SPL.
International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES
A.KARAMANLIOĞLU
A. ÇETİNKAYA
A.DALKIRAN
M. KOÇA
A.KARAMANLIOĞLU