Intuition for Principal Component Analysis (PCA)

Principal Component Analysis (PCA) is a dimension-reduction method that can be used to reduce a large set of (often correlated) variables into a smaller set of (uncorrelated) variables, called principal components, which still contain most of the information.

PCA is a concept that is traditionally hard to grasp so instead of giving you the n’th mathematical derivation I will provide you with some intuition.
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Why R for Data Science – and not Python?

There are literally hundreds of programming languages out there, e.g. the whole alphabet of one letter programming languages is taken. In the area of data science, there are two big contenders: R and Python. Now, why is this blog about R and not Python?
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OneR – Fascinating Insights through Simple Rules

We already saw the power of the OneR package in the preceding post, One Rule (OneR) Machine Learning Classification in under One Minute. Here we want to give some more examples to gain some fascinating, often counter-intuitive, insights.
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