<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Data on Samarthya</title><link>https://samarthya.me/tags/data/</link><description>Recent content in Data on Samarthya</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>© Samarthya 2018-2026. All rights reserved.</copyright><lastBuildDate>Sat, 22 Nov 2025 12:00:00 +0000</lastBuildDate><atom:link href="https://samarthya.me/tags/data/index.xml" rel="self" type="application/rss+xml"/><item><title>k-Nearest Neighbors</title><link>https://samarthya.me/posts/k-nearest-neighbors/</link><pubDate>Sat, 22 Nov 2025 12:00:00 +0000</pubDate><guid>https://samarthya.me/posts/k-nearest-neighbors/</guid><description>&lt;figure&gt;&lt;img&#10; class="my-0 rounded-md"&#10; loading="lazy"&#10; decoding="async"&#10; fetchpriority="auto"&#10; alt="Images: Courtesy GEMINI"&#10; width="600"&#10; height="600"&#10; src="https://samarthya.me/posts/k-nearest-neighbors/knn1.png"&#10; srcset="https://samarthya.me/posts/k-nearest-neighbors/knn1.png 800w, https://samarthya.me/posts/k-nearest-neighbors/knn1.png 1280w"&#10; sizes="(min-width: 768px) 50vw, 65vw"&#10; data-zoom-src="https://samarthya.me/posts/k-nearest-neighbors/knn1.png"&gt;&lt;figcaption&gt;Images: Courtesy GEMINI&lt;/figcaption&gt;&lt;/figure&gt;&#10;&lt;p&gt;KNN is the friendly, neighborhood algorithm that believes you are defined by the company you keep. It doesn’t try to learn a complex rule; it just looks around and goes with the consensus of its closest, most similar peers.&lt;/p&gt;</description></item><item><title>Data Mining Essentials</title><link>https://samarthya.me/posts/data-mining-essentials/</link><pubDate>Sat, 08 Nov 2025 12:00:00 +0000</pubDate><guid>https://samarthya.me/posts/data-mining-essentials/</guid><description>&lt;figure&gt;&lt;img&#10; class="my-0 rounded-md"&#10; loading="lazy"&#10; decoding="async"&#10; fetchpriority="auto"&#10; alt=""&#10; width="600"&#10; height="600"&#10; src="https://samarthya.me/posts/data-mining-essentials/manhattan_distance.png"&#10; srcset="https://samarthya.me/posts/data-mining-essentials/manhattan_distance.png 800w, https://samarthya.me/posts/data-mining-essentials/manhattan_distance.png 1280w"&#10; sizes="(min-width: 768px) 50vw, 65vw"&#10; data-zoom-src="https://samarthya.me/posts/data-mining-essentials/manhattan_distance.png"&gt;&lt;/figure&gt;&#10;&lt;p&gt;Whether you’re preparing for a quiz or just brushing up on fundamentals, this guide distills the key concepts from Data Mining into bite-sized, memorable chunks. Let’s dive in!&lt;/p&gt;</description></item><item><title>The Core Divide</title><link>https://samarthya.me/posts/the-core-divide/</link><pubDate>Fri, 10 Oct 2025 12:00:00 +0000</pubDate><guid>https://samarthya.me/posts/the-core-divide/</guid><description>&lt;figure&gt;&lt;img&#10; class="my-0 rounded-md"&#10; loading="lazy"&#10; decoding="async"&#10; fetchpriority="auto"&#10; alt=""&#10; width="600"&#10; height="600"&#10; src="https://samarthya.me/posts/the-core-divide/ml-1.png"&#10; srcset="https://samarthya.me/posts/the-core-divide/ml-1.png 800w, https://samarthya.me/posts/the-core-divide/ml-1.png 1280w"&#10; sizes="(min-width: 768px) 50vw, 65vw"&#10; data-zoom-src="https://samarthya.me/posts/the-core-divide/ml-1.png"&gt;&lt;/figure&gt;&#10;&lt;p&gt;At the heart of machine learning, algorithms learn from data. The key distinction between supervised and unsupervised learning is the &lt;strong&gt;type of data&lt;/strong&gt; used for training.&lt;/p&gt;</description></item><item><title>Beyond the `DataFrame`: How Parquet and Arrow Turbocharge PySpark 🚀</title><link>https://samarthya.me/posts/beyond-the-dataframe-how-parquet-and-arrow-turbocharge-pyspark/</link><pubDate>Thu, 09 Oct 2025 12:00:00 +0000</pubDate><guid>https://samarthya.me/posts/beyond-the-dataframe-how-parquet-and-arrow-turbocharge-pyspark/</guid><description>&lt;figure&gt;&lt;img&#10; class="my-0 rounded-md"&#10; loading="lazy"&#10; decoding="async"&#10; fetchpriority="auto"&#10; alt=""&#10; width="600"&#10; height="600"&#10; src="https://samarthya.me/posts/beyond-the-dataframe-how-parquet-and-arrow-turbocharge-pyspark/arrow-2.png"&#10; srcset="https://samarthya.me/posts/beyond-the-dataframe-how-parquet-and-arrow-turbocharge-pyspark/arrow-2.png 800w, https://samarthya.me/posts/beyond-the-dataframe-how-parquet-and-arrow-turbocharge-pyspark/arrow-2.png 1280w"&#10; sizes="(min-width: 768px) 50vw, 65vw"&#10; data-zoom-src="https://samarthya.me/posts/beyond-the-dataframe-how-parquet-and-arrow-turbocharge-pyspark/arrow-2.png"&gt;&lt;/figure&gt;&#10;&lt;p&gt;In my &lt;a href="https://samarthya.me/posts/pyspark-vs-pandas-choosing-right/" &gt;last post&lt;/a&gt;, we explored the divide between &lt;strong&gt;Pandas&lt;/strong&gt; (single machine) and &lt;strong&gt;PySpark&lt;/strong&gt; (distributed computing).&lt;/p&gt;&#10;&lt;blockquote&gt;&lt;p&gt;The conclusion: for massive datasets, PySpark is the clear winner.&lt;/p&gt;&#10;&lt;/blockquote&gt;</description></item><item><title>PySpark vs Pandas: Choosing Right! 🐍</title><link>https://samarthya.me/posts/pyspark-vs-pandas-choosing-right/</link><pubDate>Thu, 09 Oct 2025 12:00:00 +0000</pubDate><guid>https://samarthya.me/posts/pyspark-vs-pandas-choosing-right/</guid><description>&lt;figure&gt;&lt;img&#10; class="my-0 rounded-md"&#10; loading="lazy"&#10; decoding="async"&#10; fetchpriority="auto"&#10; alt=""&#10; width="600"&#10; height="600"&#10; src="https://samarthya.me/posts/pyspark-vs-pandas-choosing-right/pysp-1.png"&#10; srcset="https://samarthya.me/posts/pyspark-vs-pandas-choosing-right/pysp-1.png 800w, https://samarthya.me/posts/pyspark-vs-pandas-choosing-right/pysp-1.png 1280w"&#10; sizes="(min-width: 768px) 50vw, 65vw"&#10; data-zoom-src="https://samarthya.me/posts/pyspark-vs-pandas-choosing-right/pysp-1.png"&gt;&lt;/figure&gt;&#10;&lt;p&gt;As another project wraps up, bringing with it the bittersweet relief of an almost-closed chapter (and, yes, a few too many late-night coding sessions), I found myself reflecting on the tools that got us through.&lt;/p&gt;</description></item><item><title>GenAI</title><link>https://samarthya.me/posts/genai/</link><pubDate>Tue, 03 Oct 2023 12:00:00 +0000</pubDate><guid>https://samarthya.me/posts/genai/</guid><description>&lt;figure&gt;&lt;img&#10; class="my-0 rounded-md"&#10; loading="lazy"&#10; decoding="async"&#10; fetchpriority="auto"&#10; alt=""&#10; width="300"&#10; height="300"&#10; src="https://samarthya.me/posts/genai/screenshot-2023-10-03-at-9.54.03-am.png"&#10; srcset="https://samarthya.me/posts/genai/screenshot-2023-10-03-at-9.54.03-am.png 800w, https://samarthya.me/posts/genai/screenshot-2023-10-03-at-9.54.03-am.png 1280w"&#10; sizes="(min-width: 768px) 50vw, 65vw"&#10; data-zoom-src="https://samarthya.me/posts/genai/screenshot-2023-10-03-at-9.54.03-am.png"&gt;&lt;figcaption&gt;(thumbnail only — original image lost)&lt;/figcaption&gt;&lt;/figure&gt;&#10;&lt;p&gt;Artificial Intelligence (AI) has revolutionized many aspects of our lives, from virtual assistants like Siri and Alexa to self-driving cars and personalized content recommendations. One of the exciting and rapidly evolving &lt;code&gt;subfields&lt;/code&gt; of AI is Generative AI, often referred to as GenAI. In this beginner-friendly blog post, I’ll explore the basics of Generative AI, its applications, and how it’s changing the way we interact with technology.&lt;/p&gt;</description></item></channel></rss>