1<!DOCTYPE html> 2<html> 3 <head> 4 <title>Christine Maroti â Data Scientist</title> 5 6 <meta charset="utf-8" /> 7 <meta content='text/html; charset=utf-8' http-equiv='Content-Type'> 8 <meta http-equiv='X-UA-Compatible' content='IE=edge'> 9 <meta name='viewport' content='width=device-width, initial-scale=1.0, maximum-scale=1.0'> 10 11 12 <meta name="description" content="Data Scientist"> 13 <meta property="og:description" content="Data Scientist" /> 14 15 <meta name="author" content="Christine Maroti" /> 16 17 18 19 <!--[if lt IE 9]> 20
20<script src="http://html5shiv.googlecode.com/svn/trunk/html5.js"></script>
20 21 <![endif]--> 22 23 <link rel="stylesheet" type="text/css" href="/style.css" /> 24 <link rel="alternate" type="application/rss+xml" title="Christine Maroti - Data Scientist" href="/feed.xml" /> 25
25<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS-MML_HTMLorMML" type="text/javascript"></script>
25 26 27 <!-- Created with Jekyll Now - http://github.com/barryclark/jekyll-now --> 28 </head> 29 30 <body> 31 <div class="wrapper-masthead"> 32 <div class="container"> 33 <header class="masthead clearfix"> 34 <a href="/" class="site-avatar"><img src="https://avatars2.githubusercontent.com/u/30273778?v=4&s=400&u=14beb8a57a16cb3fcbbbcbe6462f2f1047447c96" /></a> 35 36 <div class="site-info"> 37 <h1 class="site-name"><a href="/">Christine Maroti</a></h1> 38 <p class="site-description">Data Scientist</p> 39 </div> 40 41 <nav> 42 <a href="/">Blog</a> 43 <a href="/about">About</a> 44 </nav> 45 </header> 46 </div> 47 </div> 48 49 <div id="main" role="main" class="container"> 50 <div class="posts"> 51 52 <article class="post"> 53 54 <h1><a href="/latex-example/">LaTeX in GitHub Readmes and Blogs</a></h1> 55 56 <div class="entry"> 57 <p>One challenge I have come across in my time at Metis and in writing blog posts is how to type mathematical expressions.</p> 58 59 60 </div> 61 62 <a href="/latex-example/" class="read-more">Read More</a> 63 </article> 64 65 <article class="post"> 66 67 <h1><a href="/project-luther/">Predicting Foreign Box Office for Movies</a></h1> 68 69 <div class="entry"> 70 <p>For project #2 at Metis we were supposed to scrape data from the internet and use it to predict something with a linear regression model. I decided to try to predict a movieâs total foreign box office, as well as what genres are more popular in certain other countries relative to others.</p> 71 72 73 </div> 74 75 <a href="/project-luther/" class="read-more">Read More</a> 76 </article> 77 78 <article class="post"> 79 80 <h1><a href="/project-benson/">MTA Turnstile Data Analysis</a></h1> 81 82 <div class="entry"> 83 <p>Our first project at Metis was essentially an exercise in exploratory data analysis. Our assignment was to help a fictional client WomenTechWomenYes (WTWY) decide at which subway stations to place canvassers, with the trifold goal of increasing attendance for their summer gala, general awareness, and fundraising. To that end, we not only focused on the number of people reached, but the kinds of people and whether they might be interested in WTWY.</p> 84 85 86 </div> 87 88 <a href="/project-benson/" class="read-more">Read More</a> 89 </article> 90 91 <article class="post"> 92 93 <h1><a href="/hello/">Hello!</a></h1> 94 95 <div class="entry"> 96 <p>Hi! Iâm Christine. I started this blog as a student at Metis Data Science 97Bootcamp in New York. This blog outlines some of my projects and other 98data science-related findings.</p> 99 100 </div> 101 102 <a href="/hello/" class="read-more">Read More</a> 103 </article> 104 105</div> 106 </div> 107 108 <div class="wrapper-footer"> 109 <div class="container"> 110 <footer class="footer"> 111 112<a href="mailto:[email protected]"><i class="svg-icon email"></i></a> 113 114 115<a href="https://github.com/cmaroti"><i class="svg-icon github"></i></a> 116 117<a href="https://www.linkedin.com/in/cmaroti"><i class="svg-icon linkedin"></i></a> 118 119 120 121 122 123 124 </footer> 125 </div> 126 </div> 127 128 129 130 </body> 131</html>
Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.