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	<title>DECISION STATS</title>
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	<description>Better Decisions === Faster Stats</description>
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		<title>Linear Discriminant Analysis (LDA) Explained: A Supervised Classification and Dimensionality Reduction Technique</title>
		<link>https://decisionstats.com/2026/07/16/linear-discriminant-analysis-lda-explained-a-supervised-classification-and-dimensionality-reduction-technique/</link>
					<comments>https://decisionstats.com/2026/07/16/linear-discriminant-analysis-lda-explained-a-supervised-classification-and-dimensionality-reduction-technique/#respond</comments>
		
		<dc:creator><![CDATA[Aviral Gupta]]></dc:creator>
		<pubDate>Fri, 17 Jul 2026 02:45:00 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Artificial Intelligence]]></category>
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					<description><![CDATA[Linear Discriminant Analysis (LDA) is a powerful supervised machine learning algorithm that serves two important purposes: classification and dimensionality reduction. Unlike Principal Component Analysis (PCA), which ignores class labels, LDA uses labeled data to find the projection that best separates different classes while preserving the most discriminative information. The primary objective of LDA is to &#8230; <a href="https://decisionstats.com/2026/07/16/linear-discriminant-analysis-lda-explained-a-supervised-classification-and-dimensionality-reduction-technique/" class="more-link">Continue reading<span class="screen-reader-text"> "Linear Discriminant Analysis (LDA) Explained: A Supervised Classification and Dimensionality Reduction&#160;Technique"</span></a>]]></description>
		
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		<title>Interview Mike Bayer SQLAlchemy #pydata #python</title>
		<link>https://decisionstats.com/2026/07/16/interview-mike-bayer-sqlalchemy-pydata-python/</link>
					<comments>https://decisionstats.com/2026/07/16/interview-mike-bayer-sqlalchemy-pydata-python/#comments</comments>
		
		<dc:creator><![CDATA[Ajay Ohri]]></dc:creator>
		<pubDate>Thu, 16 Jul 2026 08:26:01 +0000</pubDate>
				<category><![CDATA[Interviews]]></category>
		<category><![CDATA[Python]]></category>
		<guid isPermaLink="false">http://decisionstats.com/?p=13211</guid>

					<description><![CDATA[Here is an interview with Mike Bayer, the creator of popular Python package SQLAlchemy. Ajay (A)-How and why did you create SQLAlchemy? Mike (M) &#8211; SQLAlchemy was at the end of a string of various database abstraction layers I&#8217;d written over the course of my career in various languages, including Java, Perl and (badly) in &#8230; <a href="https://decisionstats.com/2026/07/16/interview-mike-bayer-sqlalchemy-pydata-python/" class="more-link">Continue reading<span class="screen-reader-text"> "Interview Mike Bayer SQLAlchemy #pydata&#160;#python"</span></a>]]></description>
		
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		<post-id xmlns="com-wordpress:feed-additions:1">13211</post-id>
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		<title>Principal Component Analysis (PCA) Explained: A Powerful Dimensionality Reduction Technique</title>
		<link>https://decisionstats.com/2026/07/15/principal-component-analysis-pca-explained-a-powerful-dimensionality-reduction-technique/</link>
					<comments>https://decisionstats.com/2026/07/15/principal-component-analysis-pca-explained-a-powerful-dimensionality-reduction-technique/#respond</comments>
		
		<dc:creator><![CDATA[Aviral Gupta]]></dc:creator>
		<pubDate>Wed, 15 Jul 2026 14:41:18 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[data]]></category>
		<category><![CDATA[Machine learning]]></category>
		<guid isPermaLink="false">http://decisionstats.com/?p=14485</guid>

					<description><![CDATA[Principal Component Analysis (PCA) is one of the most widely used unsupervised machine learning techniques for dimensionality reduction. It transforms a dataset containing many correlated features into a smaller set of uncorrelated principal components, allowing machine learning models to train faster while preserving as much information as possible. The primary objective of PCA is to &#8230; <a href="https://decisionstats.com/2026/07/15/principal-component-analysis-pca-explained-a-powerful-dimensionality-reduction-technique/" class="more-link">Continue reading<span class="screen-reader-text"> "Principal Component Analysis (PCA) Explained: A Powerful Dimensionality Reduction&#160;Technique"</span></a>]]></description>
		
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			<media:title type="html">aviralgupta0310</media:title>
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