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        <title>All Posts - ANK</title>
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        <description>All Posts | ANK</description>
        <generator>Hugo -- gohugo.io</generator><language>en</language><managingEditor>nakula.mam2023@london.edu (Nithish Kumar Akula)</managingEditor>
            <webMaster>nakula.mam2023@london.edu (Nithish Kumar Akula)</webMaster><copyright>This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</copyright><lastBuildDate>Mon, 19 Sep 2022 00:00:00 &#43;0000</lastBuildDate><atom:link href="https://nithishkumarakula.netlify.app/posts/" rel="self" type="application/rss+xml" /><item>
    <title>Are pubs the primal cause for crimes?</title>
    <link>https://nithishkumarakula.netlify.app/pubs_crimes/</link>
    <pubDate>Mon, 19 Sep 2022 00:00:00 &#43;0000</pubDate>
    <author>Nithish Kumar</author>
    <guid>https://nithishkumarakula.netlify.app/pubs_crimes/</guid>
    <description><![CDATA[Let’s load the crimes data and look at the aggregated stats along with the distribution of criminals according to the region_name.
Now, let’s examine the density plots, scatterplots and correlation between public_houses and criminals.
Using the different variables available to us, let’s create multiple models starting from base model of using mean value. The t-stat of each variable included and the adjusted R-square parameters will help in decided the appropriate model for our usecase.]]></description>
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<item>
    <title>Excess rentals in TfL bike sharing</title>
    <link>https://nithishkumarakula.netlify.app/london_bikes/</link>
    <pubDate>Mon, 19 Sep 2022 00:00:00 &#43;0000</pubDate>
    <author>Nithish Kumar</author>
    <guid>https://nithishkumarakula.netlify.app/london_bikes/</guid>
    <description><![CDATA[We often use TFL bike rides, but the frequency with which varies on various factors. Lets pull the data from TFL and examine the variance in the bikes hired from the expected monthly and weekly rentals.
Let’s pull the TFL data from their data repository, and perform EDA on this.
Let’s examine how the graphs looks like for the monthly bikes hired from 2017 to current year in comparision to the average bikes hired from 2016 to 2019, which acts as our expected rentals parameter.]]></description>
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<item>
    <title>GDP components over time and among countries</title>
    <link>https://nithishkumarakula.netlify.app/gdp_countries/</link>
    <pubDate>Mon, 19 Sep 2022 00:00:00 &#43;0000</pubDate>
    <author>Nithish Kumar</author>
    <guid>https://nithishkumarakula.netlify.app/gdp_countries/</guid>
    <description><![CDATA[Let’s look at the GDP data from the United Nations’ National Accounts Main Aggregates Database, which contains estimates of total GDP and its components for all countries from 1970 to today. At the risk of oversimplifying things, the main components of gross domestic product, GDP are personal consumption (C), business investment (I), government spending (G) and net exports (exports - imports).
We will look at how GDP and its components have changed over time, and compare different countries and how much each component contributes to that country’s GDP.]]></description>
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