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DESCRIPTION:Competition Overview:\n\nThe first edition of the annual data s
 cience hackathon\, IEEEModelthon 1.0\, aims to engage and inspire those wh
 o are passionate about developing data science and artificial intelligence
  solutions in a realistic business scenarios by engaging them in real-life
  business problems that need an innovative solutions!\n\nObjectives and Ou
 tcomes:\n\nParticipating teams will compete over a 2-weeks to develop a da
 ta science model based on the given problem description. The main objectiv
 e of this competition is to put the contestants in a participatory environ
 ment that gives them the opportunity to apply their knowledge in a real wo
 rk environments.\n\nMainly\, it is a supervised task\, and each team shoul
 d deploy their predictions as a CSV file based on the given problem statem
 ent within the [Kaggle](https://www.kaggle.com/) environment.\n\nEligibili
 ty Criteria:\n\n- Open to individuals or teams of up to 3 members.\n- Part
 icipants must be at least 18 years old and be Jordanian undergraduate univ
 ersity students.\n- Kaggle’s Competitions rules are applied.\n\nPrerequi
 sites and Recommended tools:\n\n- Basic understanding of Kaggle&#39;s environm
 ent.\n- Recommended tools: Python\, R\, Tableau\, and relevant libraries (
 e.g.\, Pandas\, Numpy\, Scikit-learn\, TensorFlow\, etc).\n\nTraining Work
 shops:\n\n- [Pandas Essentials: Data Loading &amp; Storage](https://events.vto
 ols.ieee.org/m/443883).\n- [Matplotlib Essentials: Exploratory Data Analys
 is (EDA)](https://events.vtools.ieee.org/m/444016)\n- [Scikit-Learn Essent
 ials: Data Preprocessing Techniques](https://events.vtools.ieee.org/m/4440
 85)\n- [Machine Learning Essentials: Model Training &amp; Optimization.](https
 ://events.vtools.ieee.org/m/445224)\n\nJudging Criteria:\n\nA predefined c
 ost function based on the problem case. (not specified yet).\n70% of the e
 valuation will be based on a test dataset and the rest based on the valida
 tion dataset.\n\nVirtual: https://events.vtools.ieee.org/m/443938
LOCATION:Virtual: https://events.vtools.ieee.org/m/443938
ORGANIZER:ieee.computersociety.aabu@gmail.com
SEQUENCE:96
SUMMARY:IEEEModelthon 1.0 - Jordan
URL;VALUE=URI:https://events.vtools.ieee.org/m/443938
X-ALT-DESC:Description: &lt;br /&gt;&lt;h3&gt;&lt;strong&gt;Competition Overview:&lt;/strong&gt;&lt;/h
 3&gt;\n&lt;p dir=&quot;ltr&quot; role=&quot;presentation&quot;&gt;The first edition of the annual data 
 science hackathon\,&amp;nbsp\;&lt;strong&gt;IEEEModelthon 1.0&lt;/strong&gt;\, aims to eng
 age and inspire those who are passionate about developing data science and
  artificial intelligence solutions in a realistic business scenarios by en
 gaging them in real-life business problems that need an innovative solutio
 ns!&lt;br&gt;&lt;br&gt;&lt;/p&gt;\n&lt;h3&gt;&lt;strong&gt;Objectives and Outcomes:&lt;/strong&gt;&lt;/h3&gt;\n&lt;p&gt;Pa
 rticipating teams will compete over a 2-weeks to develop a data science mo
 del based on the given problem description. The main objective of this com
 petition is to put the contestants in a participatory environment that giv
 es them the opportunity to apply their knowledge in a real work environmen
 ts.&lt;/p&gt;\n&lt;p dir=&quot;ltr&quot; role=&quot;presentation&quot;&gt;Mainly\,&amp;nbsp\; it is a supervis
 ed task\, and each team should deploy their predictions as a &lt;em&gt;CSV&lt;/em&gt; 
 file based on the given problem statement within the &lt;a title=&quot;Kaggle.com&quot;
  href=&quot;https://www.kaggle.com/&quot; target=&quot;_blank&quot; rel=&quot;noopener&quot;&gt;Kaggle&lt;/a&gt; 
 environment.&lt;br&gt;&lt;br&gt;&lt;strong id=&quot;docs-internal-guid-db780a2c-7fff-79f9-54bb
 -413afed757b6&quot;&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;h3&gt;&lt;strong&gt;Eligibility Criteria&lt;/strong&gt;:&amp;n
 bsp\;&lt;/h3&gt;\n&lt;ul&gt;\n&lt;li dir=&quot;ltr&quot; role=&quot;presentation&quot;&gt;Open to individuals or
  teams of up to 3 members.&lt;/li&gt;\n&lt;li dir=&quot;ltr&quot; role=&quot;presentation&quot;&gt;Partici
 pants must be at least &lt;strong&gt;18 years&lt;/strong&gt; old and be &lt;strong&gt;Jordan
 ian undergraduate&lt;/strong&gt; university students.&lt;/li&gt;\n&lt;li dir=&quot;ltr&quot; role=&quot;
 presentation&quot;&gt;&lt;strong&gt;Kaggle&amp;rsquo\;s&lt;/strong&gt; Competitions rules are appl
 ied.&lt;br&gt;&lt;br&gt;&lt;/li&gt;\n&lt;/ul&gt;\n&lt;h3&gt;&lt;strong&gt;Prerequisites and Recommended tools:
 &lt;/strong&gt;&lt;/h3&gt;\n&lt;ul&gt;\n&lt;li&gt;Basic understanding of Kaggle&#39;s environment.&lt;/li
 &gt;\n&lt;li&gt;Recommended tools: Python\, R\, Tableau\, and relevant libraries (e
 .g.\, Pandas\, Numpy\, Scikit-learn\, TensorFlow\, etc).&lt;br&gt;&lt;br&gt;&lt;/li&gt;\n&lt;/u
 l&gt;\n&lt;h3&gt;&lt;strong&gt;Training Workshops:&lt;/strong&gt;&lt;/h3&gt;\n&lt;ul&gt;\n&lt;li dir=&quot;ltr&quot; rol
 e=&quot;presentation&quot;&gt;&lt;a href=&quot;https://events.vtools.ieee.org/m/443883&quot;&gt;Pandas 
 Essentials: Data Loading &amp;amp\; Storage&lt;/a&gt;.&amp;nbsp\;&lt;/li&gt;\n&lt;li dir=&quot;ltr&quot; ro
 le=&quot;presentation&quot;&gt;&lt;a title=&quot;Matplotlib Essentials: Exploratory Data Analys
 is (EDA)&quot; href=&quot;https://events.vtools.ieee.org/m/444016&quot; target=&quot;_blank&quot; r
 el=&quot;noopener&quot;&gt;Matplotlib Essentials: Exploratory Data Analysis (EDA)&lt;/a&gt;&lt;/
 li&gt;\n&lt;li dir=&quot;ltr&quot; role=&quot;presentation&quot;&gt;&lt;a title=&quot;Scikit-Learn Essentials: 
 Data Preprocessing Techniques&quot; href=&quot;https://events.vtools.ieee.org/m/4440
 85&quot; target=&quot;_blank&quot; rel=&quot;noopener&quot;&gt;Scikit-Learn Essentials: Data Preproces
 sing Techniques&lt;/a&gt;&lt;/li&gt;\n&lt;li dir=&quot;ltr&quot; role=&quot;presentation&quot;&gt;&lt;a title=&quot;Mach
 ine Learning Essentials: Model Training &amp;amp\; Optimization&quot; href=&quot;https:/
 /events.vtools.ieee.org/m/445224&quot; target=&quot;_blank&quot; rel=&quot;noopener&quot;&gt;Machine L
 earning Essentials: Model Training &amp;amp\; Optimization.&lt;/a&gt;&lt;br&gt;&lt;br&gt;&lt;/li&gt;\n
 &lt;/ul&gt;\n&lt;h3&gt;&lt;strong&gt;Judging Criteria:&lt;/strong&gt;&lt;/h3&gt;\n&lt;p dir=&quot;ltr&quot; role=&quot;pre
 sentation&quot;&gt;A predefined cost function based on the problem case. (not spec
 ified yet).&lt;br&gt;70% of the evaluation will be based on a test dataset and t
 he rest based on the validation dataset.&lt;/p&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;
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