August 30, 2021 Journal article Open Access

Jyothi Vishnu Vardhan Kolla; Poorna Chandra Vemula; Vanapala Sai Mohit

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  <identifier identifierType="URL">https://zenodo.org/record/5411305</identifier>
  <creators>
    <creator>
      <creatorName>Jyothi Vishnu Vardhan Kolla</creatorName>
      <affiliation>Pursuing, BTech, Department of Computer Science and Engineering, Gitam university vishakapatnam, Andhra Pradesh</affiliation>
    </creator>
    <creator>
      <creatorName>Poorna Chandra Vemula</creatorName>
      <affiliation>Pursuing, Bachelors of Technology, Department of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu</affiliation>
    </creator>
    <creator>
      <creatorName>Vanapala Sai Mohit</creatorName>
      <affiliation>Pursuing, Bachelors of Technology, Department of Computer Science and Engineering, Gitam Institute of Technology, Visakhapatnam, Andhra Pradesh</affiliation>
    </creator>
  </creators>
  <titles>
    <title>An Optimized way to Solve Regression Problems</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject>Regression, Data processing, Noisy data, Random  sampling.</subject>
    <subject subjectScheme="issn">2249-8958</subject>
    <subject subjectScheme="handle">100.1/ijeat.E28730610521</subject>
  </subjects>
  <contributors>
    <contributor contributorType="Sponsor">
      <contributorName>Blue Eyes Intelligence Engineering  and Sciences Publication (BEIESP)</contributorName>
      <affiliation>Publisher</affiliation>
    </contributor>
  </contributors>
  <dates>
    <date dateType="Issued">2021-08-30</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5411305</alternateIdentifier>
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    <relatedIdentifier relatedIdentifierType="ISSN" relationType="IsCitedBy" resourceTypeGeneral="JournalArticle">2249-8958</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.35940/ijeat.E2873.0810621</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;In many real world scenarios, regression is a commonly used technique to predict continuous variables. In case of noisy(inconsistent) and incomplete datasets, a large number of previous works adopted complex non traditional machine learning approaches in order to get accurate predictions. However, compromising on time and space overheads. In this paper, we work with complex data yet by using traditional machine learning regression algorithms by working on data cleaning and data transformation according to the working principle of those machine learning algorithms.&lt;/p&gt;</description>
  </descriptions>
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