Practical Data Science: Reducing High Dimensional Data in R

Practical Data Science: Reducing High Dimensional Data in R

In this R course, we'll see how PCA can reduce a 5000+ variable data set down to 10 variables and barely lose accuracy! We'll look at different ways of measuring PCA's effectiveness and other ways of reducing wide data sets (those with lots of features/variables). We'll also look at the advantages and disadvantages with different ways of reducing data.

In this R course, we'll see how PCA can reduce a 5000+ variable data set into 10 variables and barely lose accuracy!

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What you will learn

• Understand various ways of reducing wide data sets
• Understand Principal Component Analysis (PCA)
• Control, tune and measure the effects of PCA

Rating: 4.35

Level: All Levels

Duration: 2.5 hours

Instructor: Manuel Amunategui