Date of Award

6-9-2017

Document Type

Thesis

Publisher

Santa Clara : Santa Clara University, 2017.

Department

Computer Engineering

First Advisor

Yi Fang

Abstract

Smarthome accessories are rapidly becoming more popular. Although many companies are making devices to take advantage of this market, most of the created smart devices are actually unintelligent. Currently, these smart home devices require meticulous, tedious configuration to get any sort of enhanced usability over their analog counterparts. We propose building a general model using machine learning and data science to automatically learn a user's smart accessory usage to predict their configuration. We have identified the requirements, collected data, recognized the risks, implemented the system, and have met the goals we set out to accomplish.

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