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<!DOCTYPE html>
<html lang="en-us">
<head>
<meta charset="UTF-8">
<title>Sangho</title>
<meta name="viewport" content="width=device-width, initial-scale=1">
<link rel="stylesheet" type="text/css" href="stylesheets/normalize.css" media="screen">
<link href='https://fonts.googleapis.com/css?family=Open+Sans:400,700' rel='stylesheet' type='text/css'>
<link rel="stylesheet" type="text/css" href="stylesheets/stylesheet.css" media="screen">
<link rel="stylesheet" type="text/css" href="stylesheets/github-light.css" media="screen">
</head>
<body>
<section class="page-header">
<h1 class="project-name">Sangho Suh</h1>
<h2 class="project-tagline"></h2>
</section>
<section class = "nav-bar">
<nav-bar>
<div id="profile" style="margin-left:1.8em">
<ul id="header">
<li > <a href="http://sanghosuh.github.io"> <span style="font-size:1.4em"> Home </span> </li>
<li > <a href="http://sanghosuh.github.io/project"> <span style="font-size:1.4em"> Project </span> </li>
<li > <a href="http://sanghosuh.github.io/research"> <span style="font-size:1.4em"> Research </span> </li>
<li > <a href="http://sanghosuh.github.io/fun"> <span style="font-size:1.4em"> Fun </span> </a> </li>
</ul>
<br>
<img src="images/profile.jpeg">
</div>
</nav-bar>
</section>
<br>
<section class="main-content">
<h3>
<a id="welcome-to-github-pages" class="anchor" href="#welcome-to-github-pages" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Educational Data Mining </h3>
<p>
This was a part of final project in Artificial Intelligence class. <br> While we were given the task of choosing two datasets from <a href="http://archive.ics.uci.edu/ml/" target="_blank">UC-Irvine Machine Learning Repository</a>, the requirements for the project were as follows: <br>
<ul>
<li> [Dataset] Classification</li>
<li> [Dataset] Multivariate </li>
<li> [Dataset] # of Attribute(10 ~ 100) </li>
<li> [Dataset] # of Instances (100 ~ 1,000, Greater than 1,000) </li>
<br>
<li> [Analysis] Use at least 4 different classifiers for comparative analysis </li>
<li> [Analysis] The final evaluation must use cross-validation </li>
<li> [Analysis] Make sure to use the concept of overfitting in final evalution </li>
<li> [Analysis] Use the results of zeroR, oneR as the baseline </li>
</ul>
The two datasets used are as follows:
<ul>
<li> Student Performance dataset </li>
<li> Turkieye Student Evaluation </li>
</ul>
NOTE: If you would like to experiment with dataset that has been pre-processed to work with Weka, you can download it from my <a href="http://www.github.com/educational_data_mining" target="_blank"> GitHub repository</a>.
</p>
<embed src="data/LA_EdMining_SanghoSuh_2013210085.pdf" width="800px" height="800px">
</section>
</body>
</html>