Please use this identifier to cite or link to this item: https://cris.library.msu.ac.zw//handle/11408/3994
Title: Relating mathematics to machine learning through algorithm development for development for big data analysis
Authors: Chirisa, Diamond Takudzwa
Keywords: relating mathematics
machine learning
algorithm development
data analysis
Issue Date: 2017
Publisher: Midland State University
Abstract: Data has increased at an exponential rate and has outpaced our capability to analyze it. However, new ways of data analysis, which thrive in big data such as Machine Learning (ML) have emerged. This study explores Machine Learning by creating a Machine Learning algorithm based on Support Vectors. This was done by converting mathematical formulations into a computer algorithm that was then used for data classification. The algorithm was evaluated and compared to other algorithms. The results of the evaluation show that the algorithm was accurate at binary classification. Comparisons to other algorithms using both the iris and breast cancer datasets show that algorithms based on Support Vectors are generally more accurate at data classification. This means that the approach that was used in this study can be used in businesses to determine whether a person will return loan or not or whether a particular student can finish a degree program or not based on past data. The study also indicated that Support Vector Machines algorithm training require more computing power as data gets bigger. Hence, it suggested use of high performance computing for big data analysis.
URI: http://hdl.handle.net/11408/3994
Appears in Collections:Bsc Mathematics Honours Degree

Files in This Item:
File Description SizeFormat 
Final Project (final).pdfFull Text2.86 MBAdobe PDFThumbnail
View/Open
Show full item record

Page view(s)

36
checked on Jul 23, 2024

Download(s)

136
checked on Jul 23, 2024

Google ScholarTM

Check


Items in MSUIR are protected by copyright, with all rights reserved, unless otherwise indicated.