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<!DOCTYPE html>
<html>
<head>
<title>AdmitAchieve</title>
<style>
body {
font-family: Arial, sans-serif;
margin: 20px;
}
h1 {
color: #333;
}
p {
line-height: 1.5;
}
</style>
</head>
<body>
<h2>Machine Learning Project: ANTICIPATION FOR US HIGHER EDUCATION</h2>
<h2>Application Title:ADMIT ACHIEVE</h2>
<p>
Welcome to the Admit Achieve project page! We are excited to share the details of our machine learning project with you.
</p>
<h2>Project Overview</h2>
<p>
In the present competitive era, admissions into master’s program in prestigious universities
has become an arduous task because of the ever-increasing competition leading to higher standards
while shortlisting aspirants and challenging for grabbing an admit.Considering the cumbersome
nature of the admission process, students often get muddled because of its various factors under
consideration. Keeping this view in context, a sound model is proposed that could predict the
chances of getting an admit, along with students leveraging the fact of forecast their chances of
an admit beforehand applying.The data has been collected by our team for which prediction is going
to be done with the help of an android app in the front end and by applying suitable data pre-processing
techniques and accuracy checks at the back. This model is prepared by using Linear Regression algorithm.
We compare the efficiency of the algorithms, the efficient algorithm chosen for mode building.
This also reducing workload for the admissions committee by automating this tedious task.
</p>
<img src="architecture.jpg" alt="">
<h2>Tools USed</h2>
<ul>
<li>Android Studio</li>
<li>Jupyter Notebook</li>
<li>Visual Studio</li>
</ul>
<h2>Languages involved</h2>
<li>Python</li>
<li>Java</li>
</ul>
<h2>UNDER THE GUIDENCE OF:</h2>
<p>Mrs.P.SHYAMALA Asst Prof<br>
Department Of CSE</p>
<h2>REACH US:</h2>
<p> Email:2011cs010293@mallareddyuniversity.ac.in</p>
<br>
<br>
</body>
</html>