Зміст курсу

Метод Головних Компонент

1. Що таке аналіз головних компонент

Вступ Практичне застосування PCA Математична ідея Приклади реальних проблем Як пояснити отримані результати?

2. Основні поняття РСА

Стандартизація Коваріаційна матриця Власні значення та власні вектори Вектор ознак та головні компоненти Бачити загальну картину

3. Побудова моделі

Scikit-learn для PCA Досліджуємо набір даних Пристосувати дані в модель Виклик

4. Аналіз результатів

Пояснити отримані компоненти А що далі?Стиснення даних Зниження рівня шуму Стиснення зображень

Приклади реальних проблем

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.

Завдання

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

Завдання

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

Все було зрозуміло?

Секція 1. Розділ 4

Приклади реальних проблем

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.

Завдання

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

Завдання

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

Все було зрозуміло?

Секція 1. Розділ 4

Приклади реальних проблем

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.

Завдання

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

Завдання

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

Все було зрозуміло?

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.

Завдання

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

Секція 1. Розділ 4

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