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    <title>DEV Community: Rahul Raj</title>
    <description>The latest articles on DEV Community by Rahul Raj (@rahulrajpl).</description>
    <link>https://dev.to/rahulrajpl</link>
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      <title>DEV Community: Rahul Raj</title>
      <link>https://dev.to/rahulrajpl</link>
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    <item>
      <title>WExDA - Web based Data exploration Tool</title>
      <dc:creator>Rahul Raj</dc:creator>
      <pubDate>Fri, 05 Jun 2020 05:32:25 +0000</pubDate>
      <link>https://dev.to/rahulrajpl/wexda-web-based-data-exploration-tool-le0</link>
      <guid>https://dev.to/rahulrajpl/wexda-web-based-data-exploration-tool-le0</guid>
      <description>&lt;p&gt;&lt;a href="https://res.cloudinary.com/practicaldev/image/fetch/s--mlpghP9T--/c_limit%2Cf_auto%2Cfl_progressive%2Cq_auto%2Cw_880/https://dev-to-uploads.s3.amazonaws.com/i/j64bi5tb6j4ieg49q3it.png" class="article-body-image-wrapper"&gt;&lt;img src="https://res.cloudinary.com/practicaldev/image/fetch/s--mlpghP9T--/c_limit%2Cf_auto%2Cfl_progressive%2Cq_auto%2Cw_880/https://dev-to-uploads.s3.amazonaws.com/i/j64bi5tb6j4ieg49q3it.png" alt="Alt Text"&gt;&lt;/a&gt;&lt;br&gt;
WExDA is a web based data exploration tool primarily useful for data preperation/ data analysis stage. This automates the EDA via web ui and is built using &lt;a href="https://discuss.streamlit.io/"&gt;streamlit&lt;/a&gt;&lt;/p&gt;

&lt;h2&gt;
  
  
  How to use
&lt;/h2&gt;

&lt;p&gt;Go to &lt;a href="https://wexda.herokuapp.com/"&gt;https://wexda.herokuapp.com/&lt;/a&gt; and start using ;)&lt;/p&gt;

&lt;h2&gt;
  
  
  Features
&lt;/h2&gt;

&lt;ul&gt;
&lt;li&gt;upload csv files upto 200MB&lt;/li&gt;
&lt;li&gt;describe data set&lt;/li&gt;
&lt;li&gt;find missing values&lt;/li&gt;
&lt;li&gt;find unique values and its count&lt;/li&gt;
&lt;li&gt;plot data via scatter, bar and pair plots&lt;/li&gt;
&lt;li&gt;plot correlation matrix in heatmap style&lt;/li&gt;
&lt;/ul&gt;

</description>
      <category>datascience</category>
      <category>machinelearning</category>
      <category>python</category>
    </item>
    <item>
      <title>Diffusion Map for manifold learning, Theory and Implementation</title>
      <dc:creator>Rahul Raj</dc:creator>
      <pubDate>Mon, 23 Mar 2020 08:11:52 +0000</pubDate>
      <link>https://dev.to/rahulrajpl/diffusion-map-for-manifold-learning-theory-and-implementation-2b81</link>
      <guid>https://dev.to/rahulrajpl/diffusion-map-for-manifold-learning-theory-and-implementation-2b81</guid>
      <description>&lt;p&gt;‘Curse of dimensionality’ is a well-known problem in Data Science, which often causes poor performance, inaccurate results, and, most importantly, a similarity measure break-down. The primary cause of this is because high dimensional datasets are typically sparse, and often a lower-dimensional structure or ‘Manifold’ would embed this data. So there is a non-linear relationship among the variables (or features or dimensions), which we need to learn to compute better similarity.&lt;/p&gt;

&lt;p&gt;Read more at &lt;a href="https://randomwalk.in/python/ml/2020/03/14/Diffusion-Map.html"&gt;https://randomwalk.in/python/ml/2020/03/14/Diffusion-Map.html&lt;/a&gt;&lt;/p&gt;

</description>
      <category>machinelearning</category>
      <category>datascience</category>
    </item>
    <item>
      <title>Hans Rosling Style analytics on Covid19</title>
      <dc:creator>Rahul Raj</dc:creator>
      <pubDate>Wed, 18 Mar 2020 11:16:54 +0000</pubDate>
      <link>https://dev.to/rahulrajpl/hans-rosling-style-analytics-on-covid19-3jd7</link>
      <guid>https://dev.to/rahulrajpl/hans-rosling-style-analytics-on-covid19-3jd7</guid>
      <description>&lt;p&gt;Hi all,&lt;/p&gt;

&lt;p&gt;I made a 'Hans Rosling' styled analytics on the Covid19 pandemic in India.&lt;br&gt;
Check out the project at&lt;br&gt;
&lt;a href="http://randomwalk.in/covid19India/"&gt;http://randomwalk.in/covid19India/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I welcome all comments/feedback at &lt;a href="https://twitter.com/rahulrajpl"&gt;https://twitter.com/rahulrajpl&lt;/a&gt;&lt;/p&gt;

</description>
      <category>datascience</category>
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