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Principal Component Analysis
course content

Course Content

Principal Component Analysis

Principal Component Analysis

1. What is Principal Component Analysis
IntroductionPractical Application of PCAMathematical IdeaExamples of Real ProblemsHow to Explain the Obtained Results?
2. Basic Concepts of PCA
StandardizationCovariance MatrixEigenvalues and EigenvectorsFeature Vector and Principal ComponentsSeeing the Big Picture
3. Model Building
Scikit-learn for PCAExplore DatasetFit Data into the ModelChallenge
4. Results Analysis
Explain Resulting ComponentsWhat’s after?Data CompressionNoise ReductionImage Compression

bookStandardization

Finally, let's start with the analysis of the PCA mathematical model.

First of all, we start by standardizing the data that the algorithm will work with. By standardization is meant the reduction of all continuous variables to a set where the mean will be equal to 0.

This is an important step because PCA cannot work properly if there is a variable in the dataset with a range of values ​​0-20 and 100-10,000, for example. PCA will start to "ignore" the characteristic with a small spread (0-20) and it will not be able to affect the result of the algorithm.

The formula for data standardization is very simple. Subtract the mean from the value of the variable and divide the result by the standard deviation:

The scikit-learn Python library allows us to do this in 1 line:

Task

Implement standardization of X array using the numpy functions np.mean() and np.std().

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Section 2. Chapter 1
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bookStandardization

Finally, let's start with the analysis of the PCA mathematical model.

First of all, we start by standardizing the data that the algorithm will work with. By standardization is meant the reduction of all continuous variables to a set where the mean will be equal to 0.

This is an important step because PCA cannot work properly if there is a variable in the dataset with a range of values ​​0-20 and 100-10,000, for example. PCA will start to "ignore" the characteristic with a small spread (0-20) and it will not be able to affect the result of the algorithm.

The formula for data standardization is very simple. Subtract the mean from the value of the variable and divide the result by the standard deviation:

The scikit-learn Python library allows us to do this in 1 line:

Task

Implement standardization of X array using the numpy functions np.mean() and np.std().

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 2. Chapter 1
toggle bottom row

bookStandardization

Finally, let's start with the analysis of the PCA mathematical model.

First of all, we start by standardizing the data that the algorithm will work with. By standardization is meant the reduction of all continuous variables to a set where the mean will be equal to 0.

This is an important step because PCA cannot work properly if there is a variable in the dataset with a range of values ​​0-20 and 100-10,000, for example. PCA will start to "ignore" the characteristic with a small spread (0-20) and it will not be able to affect the result of the algorithm.

The formula for data standardization is very simple. Subtract the mean from the value of the variable and divide the result by the standard deviation:

The scikit-learn Python library allows us to do this in 1 line:

Task

Implement standardization of X array using the numpy functions np.mean() and np.std().

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Finally, let's start with the analysis of the PCA mathematical model.

First of all, we start by standardizing the data that the algorithm will work with. By standardization is meant the reduction of all continuous variables to a set where the mean will be equal to 0.

This is an important step because PCA cannot work properly if there is a variable in the dataset with a range of values ​​0-20 and 100-10,000, for example. PCA will start to "ignore" the characteristic with a small spread (0-20) and it will not be able to affect the result of the algorithm.

The formula for data standardization is very simple. Subtract the mean from the value of the variable and divide the result by the standard deviation:

The scikit-learn Python library allows us to do this in 1 line:

Task

Implement standardization of X array using the numpy functions np.mean() and np.std().

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 2. Chapter 1
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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