Course Content

Introduction to Scikit Learn

1. The Very First Steps

Loading Data Sets SimpleImputer Ordinal Encoding

2. Scaling Numerical Data

MinMaxScaler MaxAbsScaler StandardScaler

3. Models in Scikit Learn

Prepearing Data Set 1/2 Prepearing Data Set 2/2

SimpleImputer

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.

Task

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

Import the class.
Create an instance of the class (imputer object).
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.
Fit the imputer on your data using fit() function
Impute all missing values in you data using transform() function.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

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Section 1. Chapter 2

SimpleImputer

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.

Task

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

Import the class.
Create an instance of the class (imputer object).
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.
Fit the imputer on your data using fit() function
Impute all missing values in you data using transform() function.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 1. Chapter 2

SimpleImputer

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.

Task

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

Import the class.
Create an instance of the class (imputer object).
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.
Fit the imputer on your data using fit() function
Impute all missing values in you data using transform() function.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

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.

Task

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

Import the class.
Create an instance of the class (imputer object).
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.
Fit the imputer on your data using fit() function
Impute all missing values in you data using transform() function.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 1. Chapter 2

Switch to desktop for real-world practiceContinue from where you are using one of the options below