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<journal_metadata>   <full_title>International Journal of Innovative Technology and Exploring Engineering</full_title>   <abbrev_title>IJITEE</abbrev_title>   <issn media_type='electronic'>22783075</issn>   <doi_data>     <doi>10.35940/ijitee</doi>     <resource>https://www.ijitee.org/</resource>   </doi_data> </journal_metadata> <journal_issue>  <publication_date media_type='online'>     <month>04</month>     <day>30</day>     <year>2022</year>   </publication_date>   <journal_volume>     <volume>11</volume>   </journal_volume>   <issue>5</issue> </journal_issue><!-- ============== --> <journal_article publication_type='full_text'>   <titles>     <title>Big Mart Sales Analysis</title>   </titles>   <contributors>      <organization sequence='first' contributor_role='author'>Professor, Department of Information Technology, Vidyalankar Institute of Technology, Mumbai (Maharashtra), India.</organization>    <person_name sequence='first' contributor_role='author'>      <given_name>Vidya</given_name>      <surname>Chitre</surname>    </person_name>  </contributors>    <jats:abstract xml:lang='en'>         <jats:p>In the modern era of reaching new lengths of advancement, every company and enterprise are working on their customer demands as well as their inventory management. The models used by them help them predict future demands by understanding the pattern from old sales records. Lately, everyone is abandoning the traditional prediction models for sales forecasting as it takes a prolonged amount of time to get the expected results. Therefore now the retailers keep track of their sales record in the form of a data set, which comprises price tag, outlet types, outlet location, item visibility, item outlet sales etc.</jats:p>     </jats:abstract>  <publication_date media_type='online'>     <month>04</month>     <day>30</day>     <year>2022</year>   </publication_date>   <pages>     <first_page>8</first_page>     <last_page>11</last_page>   </pages>   <crossmark>     <crossmark_version>CC BY-NC-ND 4.0</crossmark_version>     <crossmark_policy>10.35940/BEIESP.CrossMarkPolicy</crossmark_policy>     <crossmark_domains>       <crossmark_domain>          <domain>www.ijitee.org</domain>       </crossmark_domain>     </crossmark_domains>     <crossmark_domain_exclusive>true</crossmark_domain_exclusive>   </crossmark>   <doi_data>     <doi>10.35940/ijitee.C9833.0411522</doi>     <resource>https://www.ijitee.org/portfolio-item/e98330411522/</resource>   </doi_data> </journal_article>
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