Principal Component Analysis

Principal Component Analysis

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1. What is Principal Component Analysis

Let's take a look at PCA (Principal Component Analysis) from afar. In this section, you will learn what PCA is, why this data processing method is needed and see the real problems it solves.

Practical Application of PCA

Mathematical Idea

Examples of Real Problems

How to Explain the Obtained Results?

2. Basic Concepts of PCA

Learn about the design of the PCA algorithm. This section will show each stage in detail and simply.

Standardization

Covariance Matrix

Eigenvalues and Eigenvectors

Feature Vector and Principal Components

Seeing the Big Picture

3. Model Building

Set up your first PCA model using the Python library and solve the challenge provided for us.

Scikit-learn for PCA

Explore Dataset

Fit Data into the Model

4. Results Analysis

It's time to find out how we can explain the results. What do the first, second and third components tell us? What can we use the data for in the future?

Explain Resulting Components

What’s after?

Data Compression

Noise Reduction

Image Compression

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

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.

Task

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.

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

Everything was clear?

Section 3. Chapter 4

toggle bottom row

Challenge

Task

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.

Task

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.

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

Everything was clear?

Section 3. Chapter 4

toggle bottom row

Challenge

Task

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.

Task

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.

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

Everything was clear?

Task

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.

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

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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