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Principal Component Analysis
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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

bookExplore Dataset

Now we will take a closer look at the creation of a PCA model using the example of one dataset. As a dataset, we will use wine from the scikit-learn set. It contains 13 wine characteristics and 3 classes. It is especially convenient for us because there are no categorical variables in it.

Let's load the dataset:

Now let's explore the dataset to understand what data we are working with. Let's convert the numpy array X to a pandas dataframe and check the amount of missing data:

To get a complete description of each column (mean, standard deviation, etc.), use the .describe() method:

Before loading the dataset into the PCA model, let's process our data. Based on the previous lessons, you may have noticed that an important step is data standardization. We implement this using the StandardScaler() class:

Task

Read the data from the train.csv (from web) file. Remove the "Id" column from the dataset and standardize it.

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Section 3. Chapter 2
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bookExplore Dataset

Now we will take a closer look at the creation of a PCA model using the example of one dataset. As a dataset, we will use wine from the scikit-learn set. It contains 13 wine characteristics and 3 classes. It is especially convenient for us because there are no categorical variables in it.

Let's load the dataset:

Now let's explore the dataset to understand what data we are working with. Let's convert the numpy array X to a pandas dataframe and check the amount of missing data:

To get a complete description of each column (mean, standard deviation, etc.), use the .describe() method:

Before loading the dataset into the PCA model, let's process our data. Based on the previous lessons, you may have noticed that an important step is data standardization. We implement this using the StandardScaler() class:

Task

Read the data from the train.csv (from web) file. Remove the "Id" column from the dataset and standardize it.

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 2
toggle bottom row

bookExplore Dataset

Now we will take a closer look at the creation of a PCA model using the example of one dataset. As a dataset, we will use wine from the scikit-learn set. It contains 13 wine characteristics and 3 classes. It is especially convenient for us because there are no categorical variables in it.

Let's load the dataset:

Now let's explore the dataset to understand what data we are working with. Let's convert the numpy array X to a pandas dataframe and check the amount of missing data:

To get a complete description of each column (mean, standard deviation, etc.), use the .describe() method:

Before loading the dataset into the PCA model, let's process our data. Based on the previous lessons, you may have noticed that an important step is data standardization. We implement this using the StandardScaler() class:

Task

Read the data from the train.csv (from web) file. Remove the "Id" column from the dataset and standardize it.

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!

Now we will take a closer look at the creation of a PCA model using the example of one dataset. As a dataset, we will use wine from the scikit-learn set. It contains 13 wine characteristics and 3 classes. It is especially convenient for us because there are no categorical variables in it.

Let's load the dataset:

Now let's explore the dataset to understand what data we are working with. Let's convert the numpy array X to a pandas dataframe and check the amount of missing data:

To get a complete description of each column (mean, standard deviation, etc.), use the .describe() method:

Before loading the dataset into the PCA model, let's process our data. Based on the previous lessons, you may have noticed that an important step is data standardization. We implement this using the StandardScaler() class:

Task

Read the data from the train.csv (from web) file. Remove the "Id" column from the dataset and standardize it.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 3. Chapter 2
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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