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segments.html
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<html>
<head>
<link rel="stylesheet" href="styles.css">
</head>
<body>
<div class="navbar">
<a href="index.html">Home</a>
<a href="segments.html">Popular Specifications and Data Segments</a>
<a href="other.html"> Other queries</a>
</div>
<div class="main">
<div class="heading">
<h1>Segments</h1>
</div>
<h2>Analysis of SIAM segments</h2>
<p>As per SIAM (Society of Indian Automobile Manufacturers)
classification of segment is done on the basis of the length and body type of the vehicle. These
details are taken from Team-BHP.
</p>
<ul>
<li><b>A Segment - </b>Small/mini hatchbacks. Ex
- Renault Kwid, Maruti S-Presso/Alto 800</li>
<li><b>B1 Segment - </b>Regular hatchbacks. eg. Maruti Swift/WagonR, Hyundai Grand i10 Nios,
Tata
Tiago etc.
</li>
<li><b>B2 Segment -</b> Premium hatchbacks and compact SUVs. eg. Hyundai i20, Maruti Baleno, etc.
</li>
<li><b>C1 Segment -</b> Entry level sedans and larger SUVs. eg. KIA Sonet, Hyundai Venue,
Tata
Nexon, etc.</li>
<li><b>C2 Segment - </b>Proper sedans and also includes the bigger/premium SUVs. eg. Honda
City,
Maruti Ciaz, Skoda Slavia/Kushaq, etc.
</li>
<li><b>D1 Segment -</b> Larger and more powerful sedans and the larger SUVs. eg. Skoda
Octavia,
Honda Civic,Jeep Compass, etc.</li>
<li><b>D2 Segment - </b>Premium sedans and SUVs
eg. Honda Accord, Toyota Camry/Fortuner, Ford Endeavor
</li>
<li><b>Premium -</b> Luxury brands and their Sedans and SUVs. eg.s Audi, Lexus, BMW, Porsche, Jaguar etc.
</li>
<li><b>Utility - </b>Big rugged 4X4's. eg. Mahindra Thar, Force Gurkha,
Toyota Hilux, Isuzu D-Max etc.
</li>
</ul>
<p>Using ANOVA, we can see that the segments as classified by SIAM vary significantly from one another.
</p>
<br>
<img src="ANOVA.png" alt="ANOVA">
<h2>Segmentation as per K-means Clustering</h2>
<p>Divided the dataset into 7 clusters using the K-means algorithm in unsupervised learning. This was done using
the BigML extension in Google Sheets.
</p>
<ul>
<li>Removed premium and utility cars while clustering because there are a wide range of cars in those
segments. The clustering wasn't very effective due to this.</li>
<li>Considered showroon price, displacement and volume for clustering as they produced the most accurate
clusters. </li>
</ul><br>
<h2>Comparison of SIAM segments with K-means Clustering</h2>
<p>The <b>K-means clustering</b> algorithm provided better results while grouping cars which have models spread
across a wide price range. For example the <b>Tata Nexon</b> has models from 7 lakh to 12 lakh. In the SIAM
categorization all these cars come under the C1 segment.
</p>
<p>But in the K-means clustering they are divided into 2 segments. Thus, it is a more accurate representation of
the dataset.</p>
<br>
<iframe width="715" height="371" seamless frameborder="0" scrolling="no"
src="https://docs.google.com/spreadsheets/d/e/2PACX-1vS-DFxzW7OHIUOzHcR1-qEXJPOMdRWJETUFrb9WcRdKo1S6b6_qf7qz5zdiR-29gDvjLUNXb-4X5eLi/pubchart?oid=1100257143&format=interactive"></iframe><br><br>
<h2>Charts</h2>
<p>The following charts provide a comparative study of SIAM and K-means clustering</p>
<ol>
<li><a href="general.html">General</a></li><br>
<li><a href="performance.html">Performance</a></li><br>
<li><a href="safety.html">Safety</a></li><br>
<li><a href="infotainment.html">Infotainment</a></li><br>
<li><a href="luxury.html">Luxury</a></li><br>
</ol>
</div>
</body>
</html>