Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/75432
Title: Visual analytics for large-scale datasets
Authors: Gani, Reinaldo
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2018
Abstract: For the last several years, we have been seeing rapid increase of digital transformation. One of the evidence is that the more and more individuals frequently using their smart devices such smartphones and tablets in many aspects of their life such as work, school, recreation, communication, and household. All of these interactions between individuals and smart devices can produce huge amount of usage data, which contain rich and valuable information about smart device owners’ usage habits and behaviour in their daily life that yet to be discovered. Data that were created from these interactions are ranging from exercise logs, archives of interactions with colleagues in many different social media, to our hobby and interest from time to time. There is an enormous potential that can be explored to use these data to understand individuals and businesses better. In the first part of this project, the aim is to create an interactive visualisation analytics dashboard of Yelp dataset to extract and analyse visitors’ checkins in city of Las Vegas and visitors distribution of businesses that potentially can help business owner to execute their operations better. An interactive visualisation means that individuals can change multiple parameters to zoom in or zoom out to the data they think interesting so that they can uncover hidden information. The second part of this project is to create a predictive analytic in the form of recommendation system. Recommendation system built in this project will recommend best restaurants in Las Vegas based on user’s key term as input and also collected reviews for all businesses in Yelp dataset. Recommended restaurants will be displayed on Las Vegas map to help user to make better decision. Lastly, the project proposes various improvement of recommendation system in the future.
URI: http://hdl.handle.net/10356/75432
Schools: School of Electrical and Electronic Engineering 
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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