Зміст курсу
Метод Головних Компонент
Метод Головних Компонент
Досліджуємо набір даних
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:
Завдання
Read the data from the train.csv (from web) file. Remove the "Id" column from the dataset and standardize it.
Завдання
Read the data from the train.csv (from web) file. Remove the "Id" column from the dataset and standardize it.
Все було зрозуміло?
Досліджуємо набір даних
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:
Завдання
Read the data from the train.csv (from web) file. Remove the "Id" column from the dataset and standardize it.
Завдання
Read the data from the train.csv (from web) file. Remove the "Id" column from the dataset and standardize it.
Все було зрозуміло?
Досліджуємо набір даних
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:
Завдання
Read the data from the train.csv (from web) file. Remove the "Id" column from the dataset and standardize it.
Завдання
Read the data from the train.csv (from web) file. Remove the "Id" column from the dataset and standardize it.
Все було зрозуміло?
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:
Завдання
Read the data from the train.csv (from web) file. Remove the "Id" column from the dataset and standardize it.
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