Course Content
Principal Component Analysis
Principal Component Analysis
1. What is Principal Component Analysis
3. Model Building
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.
Task
Read the train.csv
dataset (from web) and create a PCA model for it. There should be 4 main components.
Task
Read the train.csv
dataset (from web) and create a PCA model for it. There should be 4 main components.
Everything was clear?
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.
Task
Read the train.csv
dataset (from web) and create a PCA model for it. There should be 4 main components.
Task
Read the train.csv
dataset (from web) and create a PCA model for it. There should be 4 main components.
Everything was clear?
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.
Task
Read the train.csv
dataset (from web) and create a PCA model for it. There should be 4 main components.
Task
Read the train.csv
dataset (from web) and create a PCA model for it. There should be 4 main components.
Everything was clear?
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.
Task
Read the train.csv
dataset (from web) and create a PCA model for it. There should be 4 main components.