View in EDS PDF Full Text

House Price Forecasting Using Machine Learning.

Saved in:
Bibliographic Details
Title: House Price Forecasting Using Machine Learning.
Authors: Goyal, Aman1, Kumar, Utkarsh1
Source: Turkish Online Journal of Qualitative Inquiry. 2021, Vol. 12 Issue 6, p9944-9948. 5p.
Subject Terms: Home prices, Decision trees, Computer engineering, Real property sales & prices
Geographic Terms: Mumbai (India)
Abstract: The real estate market is one of the world's most price-sensitive, and it is always changing. It's one of the most critical fields where computer technology can be used. exactitude in learning how to improve and predict costs in high-risk situations The aim of the paper is to make a future prediction. This system is helps in finding the starting price of a property based on geographical factors Through analysing past data patterns and price ranges in the industry, as well as future developments, The potential costs will be calculated. The scope of this investigation is as follows: You can forecast house prices in Mumbai using a decision tree. [ABSTRACT FROM AUTHOR]
Copyright of Turkish Online Journal of Qualitative Inquiry is the property of Turkish Online Journal of Qualitative Inquiry and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
FullText Links:
  – Type: pdflink
Text:
  Availability: 0
Header DbId: ehh
DbLabel: Education Research Complete
An: 160450903
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: House Price Forecasting Using Machine Learning.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Goyal%2C+Aman%22">Goyal, Aman</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Kumar%2C+Utkarsh%22">Kumar, Utkarsh</searchLink><relatesTo>1</relatesTo>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Turkish+Online+Journal+of+Qualitative+Inquiry%22">Turkish Online Journal of Qualitative Inquiry</searchLink>. 2021, Vol. 12 Issue 6, p9944-9948. 5p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Home+prices%22">Home prices</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+trees%22">Decision trees</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+engineering%22">Computer engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Real+property+sales+%26+prices%22">Real property sales & prices</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Mumbai+%28India%29%22">Mumbai (India)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The real estate market is one of the world's most price-sensitive, and it is always changing. It's one of the most critical fields where computer technology can be used. exactitude in learning how to improve and predict costs in high-risk situations The aim of the paper is to make a future prediction. This system is helps in finding the starting price of a property based on geographical factors Through analysing past data patterns and price ranges in the industry, as well as future developments, The potential costs will be calculated. The scope of this investigation is as follows: You can forecast house prices in Mumbai using a decision tree. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Turkish Online Journal of Qualitative Inquiry is the property of Turkish Online Journal of Qualitative Inquiry and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=160450903
RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 5
        StartPage: 9944
    Subjects:
      – SubjectFull: Home prices
        Type: general
      – SubjectFull: Decision trees
        Type: general
      – SubjectFull: Computer engineering
        Type: general
      – SubjectFull: Real property sales & prices
        Type: general
      – SubjectFull: Mumbai (India)
        Type: general
    Titles:
      – TitleFull: House Price Forecasting Using Machine Learning.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Goyal, Aman
      – PersonEntity:
          Name:
            NameFull: Kumar, Utkarsh
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 07
              Text: 2021
              Type: published
              Y: 2021
          Identifiers:
            – Type: issn-print
              Value: 13096591
          Numbering:
            – Type: volume
              Value: 12
            – Type: issue
              Value: 6
          Titles:
            – TitleFull: Turkish Online Journal of Qualitative Inquiry
              Type: main
ResultId 1