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Prediction of cost overruns using ensemble methods in data mining and text mining algorithms

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TitleInfo
Title
Prediction of cost overruns using ensemble methods in data mining and text mining algorithms
Name (type = personal)
NamePart (type = family)
Ramesh
NamePart (type = given)
Prathiksha
NamePart (type = date)
1990-
DisplayForm
Prathiksha Ramesh
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Williams
NamePart (type = given)
Trefor
DisplayForm
Trefor Williams
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Gong
NamePart (type = given)
Jie
DisplayForm
Jie Gong
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Gonzales
NamePart (type = given)
Eric
DisplayForm
Eric Gonzales
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2014
DateOther (qualifier = exact); (type = degree)
2014-01
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
In competitive bidding in the United States, the lowest bid is most often than not selected to perform the project. However, the lowest bidder tends to undervalue the costs in order to win the bid and as a result may incur significant cost increases during the construction life cycle due to change orders. For project owners to accurately estimate the actual project cost and to predict the bid that is close to the actual project, there is an urgent need for new decision aids to analyze the bidding patterns. The goal of this research has been to select the predictive features in a bid package to help minimize the cost overruns with the help of open source data mining software. The features were selected based on correlation and regression analysis by studying the p-values and r-squared values. The data set was then prepared with only the features that were affecting the output, which in our case were the cost overruns. The output is divided into 4 classes depending on the percentage of overrun. The learning algorithms used for prediction were neural networks, support vector machines, decision trees along with the ensemble methods. The empirical study of the prediction models suggest an efficiency of up to 50% in predicting whether a project will have cost overruns and what is the approximate range of percentage overrun.
Subject (authority = RUETD)
Topic
Civil and Environmental Engineering
Subject (authority = ETD-LCSH)
Topic
Letting of contracts--Data processing
Subject (authority = ETD-LCSH)
Topic
Data mining
Subject (authority = ETD-LCSH)
Topic
Cost
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_5204
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
viii, 41 p. : ill.
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Prathiksha Ramesh
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/T3WW7FR1
Genre (authority = ExL-Esploro)
ETD graduate
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Ramesh
GivenName
Prathiksha
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2013-12-12 16:46:25
AssociatedEntity
Name
Prathiksha Ramesh
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

RULTechMD (ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem (VERSION = 5.1)
windows xp
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