This capstone project report covers the research and development of Smart Anomaly Detection and Subscriber Analysis in the domain of Online Video Data Analytics. In the co-written portions of this document, we discuss the projected commercialization success of our products by analyzing worldwide trends in online video, presenting a competitive business strategy, and describing several approaches towards the management of our intellectual property. In the individually written portion of this document, we discuss our implementation of two machine learning techniques, k-means clustering and logistic regression, and give detailed evaluation of these techniques on our dataset.
Title
Online Video Data Analytics
Published
2015-05-14
Full Collection Name
Electrical Engineering & Computer Sciences Technical Reports
Other Identifiers
EECS-2015-112
Type
Text
Extent
58 p
Archive
The Engineering Library
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