Contenido del Curso

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

Examples of Real Problems

Let's look at a real-life example of the application of the PCA method. Import the libraries with which we will work:

Next, we read the train.csv file (from web), which contains data on house sales with the characteristics of houses and their prices:

Let's process our data. This process includes dropping many characteristics from the dataset (we will leave only 10 variables - this way it will be easier for us to work with the results obtained so that there are not too many characteristics), as well as data scaling:

Let's create a PCA model:

Now, to explain the results obtained, we will create a heat map of the factor loading. In the next section, we will learn why we need it.

In just a couple of steps, we reduced the dimension of the dataset from 10 characteristics to 3! In the next chapter, we will try to interpret the results of PCA.

Tarea

Read the train.csv dataset (from web) and create a PCA model for it. There should be 4 main components.

Tarea

Read the train.csv dataset (from web) and create a PCA model for it. There should be 4 main components.

¿Todo estuvo claro?

Sección 1. Capítulo 4

Examples of Real Problems

Let's look at a real-life example of the application of the PCA method. Import the libraries with which we will work:

Next, we read the train.csv file (from web), which contains data on house sales with the characteristics of houses and their prices:

Let's create a PCA model:

Now, to explain the results obtained, we will create a heat map of the factor loading. In the next section, we will learn why we need it.

In just a couple of steps, we reduced the dimension of the dataset from 10 characteristics to 3! In the next chapter, we will try to interpret the results of PCA.

Tarea

Read the train.csv dataset (from web) and create a PCA model for it. There should be 4 main components.

Tarea

Read the train.csv dataset (from web) and create a PCA model for it. There should be 4 main components.

¿Todo estuvo claro?

Sección 1. Capítulo 4

Examples of Real Problems

Let's look at a real-life example of the application of the PCA method. Import the libraries with which we will work:

Next, we read the train.csv file (from web), which contains data on house sales with the characteristics of houses and their prices:

Let's create a PCA model:

Now, to explain the results obtained, we will create a heat map of the factor loading. In the next section, we will learn why we need it.

In just a couple of steps, we reduced the dimension of the dataset from 10 characteristics to 3! In the next chapter, we will try to interpret the results of PCA.

Tarea

Read the train.csv dataset (from web) and create a PCA model for it. There should be 4 main components.

Tarea

Read the train.csv dataset (from web) and create a PCA model for it. There should be 4 main components.

¿Todo estuvo claro?

Let's look at a real-life example of the application of the PCA method. Import the libraries with which we will work:

Next, we read the train.csv file (from web), which contains data on house sales with the characteristics of houses and their prices:

Let's create a PCA model:

Now, to explain the results obtained, we will create a heat map of the factor loading. In the next section, we will learn why we need it.

In just a couple of steps, we reduced the dimension of the dataset from 10 characteristics to 3! In the next chapter, we will try to interpret the results of PCA.

Tarea

Read the train.csv dataset (from web) and create a PCA model for it. There should be 4 main components.

Sección 1. Capítulo 4

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