Principal Component Analysis

Course Content

Principal Component Analysis

Principal Component Analysis

1. What is Principal Component Analysis

Introduction Practical Application of PCA Mathematical Idea Examples of Real Problems How to Explain the Obtained Results?

2. Basic Concepts of PCA

Standardization Covariance Matrix Eigenvalues and Eigenvectors Feature Vector and Principal Components Seeing the Big Picture

3. Model Building

Scikit-learn for PCA Explore Dataset Fit Data into the Model Challenge

4. Results Analysis

Explain Resulting Components What’s after?Data Compression Noise Reduction Image Compression

Challenge

Task

Swipe to start coding

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

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

Solution

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Section 3. Chapter 4

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Challenge

Task

Swipe to start coding

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

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

Solution

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 3. Chapter 4

Switch to desktop for real-world practiceContinue from where you are using one of the options below

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