Travel destinations and route prediction tool: case of dynamic and personalized ecosystem

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dc.contributor.authorKiragu, Phillis Wacera
dc.date.accessioned2019-10-28T11:45:48Z
dc.date.available2019-10-28T11:45:48Z
dc.date.issued2019
dc.descriptionThesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Information Technology (MSIT) at Strathmore Universityen_US
dc.description.abstractTravelling is part of every individual’s life today. Travelling has become part of people’s life goals. These travels range from short vacations to yearlong round the world trips. There are many reasons why people travel, most of which fall in the broader categories of either business or leisure. People have been travelling for ages and each day attempt to come up with better ways of planning. However, there are many issues that come up when travelling or planning to travel to new places. One of the major issues is not knowing which destinations fits them best and the most optimal route to use. As such most people end up going to popular destinations that are traditionally known. Most systems today are very good at guiding the user on what to do when they get to a certain destination but they do little in identifying the destinations in the first place. This study proposes a natural language processing model that takes in a user’s preferences in terms of factors such a budget and weather and returns a list of predicted destinations for that user. Natural Language Processing is carried out by training a neural network model that detects similarities based on word vectors. The end result is a prediction of destinations suitable for a user based on their personal preferences.en_US
dc.identifier.urihttp://hdl.handle.net/11071/6707
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.subjectTravellingen_US
dc.subjectDestinationsen_US
dc.subjectRoutesen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectNatural language processingen_US
dc.subjectPredictionen_US
dc.titleTravel destinations and route prediction tool: case of dynamic and personalized ecosystemen_US
dc.typeThesisen_US
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MSIT Theses and Dissertations (2019)