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Introduction to Scikit Learn
course content

Contenido del Curso

Introduction to Scikit Learn

Introduction to Scikit Learn

1. The Very First Steps
Loading Data SetsSimpleImputerOrdinal Encoding
2. Scaling Numerical Data
MinMaxScaler MaxAbsScalerStandardScaler
3. Models in Scikit Learn
Prepearing Data Set 1/2Prepearing Data Set 2/2

bookSimpleImputer

We figured out the identification of missing values. Time now to find out what to do with them and how.

SimpleImputer - it is a class from the scikit-learn library, and which is used to work with the missing values.

SimpleImputer(). This method replaces the missing values with more logical values. It has such main arguments, let's look at them.

  • missing_values - a way to represent missing values, by default is NaN, but as we have already said, it can be for example 0.
  • strategy - here we indicate which values we will replace with. It can be mean(default), median, most_frequent and constant.
  • fill_value - a constant value, with which we will replace the missing values, if we chose strategy = constant.

We learn fit() and transform() functions a little more later.

Tarea

Let's try to fill the empty space in your small dataset.To use SimpleImputer you have to implement the next steps:

  1. Import the class.
  2. Create an instance of the class (imputer object).
  3. Specify the parameters you need, especially: we see that here the missing values are represented by NaN, so replace them with the constant value 15.
  4. Fit the imputer on your data using fit() function
  5. Impute all missing values in you data using transform() function.

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¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

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Sección 1. Capítulo 2
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bookSimpleImputer

We figured out the identification of missing values. Time now to find out what to do with them and how.

SimpleImputer - it is a class from the scikit-learn library, and which is used to work with the missing values.

SimpleImputer(). This method replaces the missing values with more logical values. It has such main arguments, let's look at them.

  • missing_values - a way to represent missing values, by default is NaN, but as we have already said, it can be for example 0.
  • strategy - here we indicate which values we will replace with. It can be mean(default), median, most_frequent and constant.
  • fill_value - a constant value, with which we will replace the missing values, if we chose strategy = constant.

We learn fit() and transform() functions a little more later.

Tarea

Let's try to fill the empty space in your small dataset.To use SimpleImputer you have to implement the next steps:

  1. Import the class.
  2. Create an instance of the class (imputer object).
  3. Specify the parameters you need, especially: we see that here the missing values are represented by NaN, so replace them with the constant value 15.
  4. Fit the imputer on your data using fit() function
  5. Impute all missing values in you data using transform() function.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 1. Capítulo 2
toggle bottom row

bookSimpleImputer

We figured out the identification of missing values. Time now to find out what to do with them and how.

SimpleImputer - it is a class from the scikit-learn library, and which is used to work with the missing values.

SimpleImputer(). This method replaces the missing values with more logical values. It has such main arguments, let's look at them.

  • missing_values - a way to represent missing values, by default is NaN, but as we have already said, it can be for example 0.
  • strategy - here we indicate which values we will replace with. It can be mean(default), median, most_frequent and constant.
  • fill_value - a constant value, with which we will replace the missing values, if we chose strategy = constant.

We learn fit() and transform() functions a little more later.

Tarea

Let's try to fill the empty space in your small dataset.To use SimpleImputer you have to implement the next steps:

  1. Import the class.
  2. Create an instance of the class (imputer object).
  3. Specify the parameters you need, especially: we see that here the missing values are represented by NaN, so replace them with the constant value 15.
  4. Fit the imputer on your data using fit() function
  5. Impute all missing values in you data using transform() function.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

We figured out the identification of missing values. Time now to find out what to do with them and how.

SimpleImputer - it is a class from the scikit-learn library, and which is used to work with the missing values.

SimpleImputer(). This method replaces the missing values with more logical values. It has such main arguments, let's look at them.

  • missing_values - a way to represent missing values, by default is NaN, but as we have already said, it can be for example 0.
  • strategy - here we indicate which values we will replace with. It can be mean(default), median, most_frequent and constant.
  • fill_value - a constant value, with which we will replace the missing values, if we chose strategy = constant.

We learn fit() and transform() functions a little more later.

Tarea

Let's try to fill the empty space in your small dataset.To use SimpleImputer you have to implement the next steps:

  1. Import the class.
  2. Create an instance of the class (imputer object).
  3. Specify the parameters you need, especially: we see that here the missing values are represented by NaN, so replace them with the constant value 15.
  4. Fit the imputer on your data using fit() function
  5. Impute all missing values in you data using transform() function.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
Sección 1. Capítulo 2
Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
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