brickbreakgame/post-ds.html at main · full-stack-developer-SURE-TRUST/brickbreakgame · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
<!DOCTYPE html>
<html>
<head>
	<title>Post Name</title>


	<meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1, minimum-scale=1" />

	<link href="https://fonts.googleapis.com/css2?family=Russo+One&display=swap" rel="stylesheet">

	<link href="https://fonts.googleapis.com/css2?family=Roboto+Mono:wght@500&display=swap" rel="stylesheet">

	<link rel="stylesheet" type="text/css" href="default.css">


	<style type="text/css">
		.img-container{
			max-width: 600px;
            float: right;
		}

		img{
			width: 100%
		}

	</style>

</head>
<body>
	<div class="nav-wrapper">

		<!-- Link around dots-wrapper added after tutorial video -->
		<a href="index.html">
			<div class="dots-wrapper">
				<div id="dot-1" class="browser-dot"></div>
				<div id="dot-2" class="browser-dot"></div>
				<div id="dot-3" class="browser-dot"></div>
			</div>
		</a>

		<ul id="navigation">
			<li><a href="index.html#contact">Contact</a></li>

		</ul>
	</div>

	<div class="main-container">
		<br>

		<div class="img-container">
			<img src="images/data-science.png">
		</div>

		<h3>Data Science project</h3>

		<p>The project which i did was a customer segmentation which is more popular in Unsupervised Machine learning which was did with K-means clustering, Using clustering, identified segments of customers to target the potential user base. By dividing customers into groups according to common characteristics like gender, age, interests, and spending habits so they can market to each group effectively.</p>
        <p>And i started building my first project on K-means Clustering</p>
		<h5>Technologies:</h5>
		<ul>
			<li>- Python</li>
			<li>- Pandas, numpy, Matplotlib</li>
		</ul>


	</div>

</body>
</html>