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Principal Component Analysis
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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

bookChallenge

Task

The task is to process the dataset and create a principal component analysis model with 3 components.

  1. Load the train.csv (from web) dataset.
  2. Drop the 'Id' column.
  3. Drop columns that contain NaN values.
  4. Standardize the dataset.
  5. Create a PCA model with 3 components for the dataset.

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Section 3. Chapter 4
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bookChallenge

Task

The task is to process the dataset and create a principal component analysis model with 3 components.

  1. Load the train.csv (from web) dataset.
  2. Drop the 'Id' column.
  3. Drop columns that contain NaN values.
  4. Standardize the dataset.
  5. Create a PCA model with 3 components for the dataset.

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 3. Chapter 4
toggle bottom row

bookChallenge

Task

The task is to process the dataset and create a principal component analysis model with 3 components.

  1. Load the train.csv (from web) dataset.
  2. Drop the 'Id' column.
  3. Drop columns that contain NaN values.
  4. Standardize the dataset.
  5. Create a PCA model with 3 components for the dataset.

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!

Task

The task is to process the dataset and create a principal component analysis model with 3 components.

  1. Load the train.csv (from web) dataset.
  2. Drop the 'Id' column.
  3. Drop columns that contain NaN values.
  4. Standardize the dataset.
  5. Create a PCA model with 3 components for the dataset.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 3. Chapter 4
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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