{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/111438"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/111438","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Analytics for hotels : demand prediction and decision optimization","abstract":"The thesis presents the work with a hotel company, as an example of how machine learning techniques can be applied to improve demand predictions and help a hotel property to make better decisions on its pricing and capacity allocation strategies. To solve the decision optimization problem, we first build a random forest model to predict demand under given prices, and then plug the predictions into a mixed integer program to optimize the prices and capacity allocation decisions. 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