Ordinal Encoding

Features can be divided into categorical and numerical.

A categorical feature is a feature whose value can be attributed to any group, but the order of the values in this group is completely unimportant. Between the values of categorical features it is impossible to establish the relationship > or < ('greater' or 'less').

The value of a numeric feature is a scalar. Between the values of numeric features it is possible to establish the relationship 'greate' or 'less'.

КАРТИНКА???

Scikit-learn does not support processing of categorical features. So we should move to numerical representation

We have the two most techniques to move to numerical representation: an Ordinal Encoding and an One-Hot Encoding. Let's get acquainted with the one of them Ordinal Encoding - the point of this encoding is that each unique value of the category is encoded with an integer number. For example: python is 1, SQL is 2, Java is 3.

Now, let's look at the example how to implelemt this encoding.


              123456789101112131415
            
# example of a ordinal encoding
import pandas as pd
from sklearn.preprocessing import OrdinalEncoder
# define data
data = pd.read_csv('C:/Users/User1/Desktop/РОБОТА/Data.csv')
print(data)

# define ordinal encoding
encoder = OrdinalEncoder()

# transform data
result = encoder.fit(data)
result = result.transform(data)

print(result)

It is time for an example.

Analysis

We see that here the missing values are represented by zeros(missing_values = 0), we replace them with the mean value(strategy ='mean') of the column in which the missing value is located.

Tâche

Swipe to start coding

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.

Solution

Passez à un bureau pour une pratique réelleContinuez d'où vous êtes en utilisant l'une des options ci-dessous

Tout était clair ?

Merci pour vos commentaires !

Section 1. Chapitre 3

Ordinal Encoding

Features can be divided into categorical and numerical.

The value of a numeric feature is a scalar. Between the values of numeric features it is possible to establish the relationship 'greate' or 'less'.

КАРТИНКА???

Scikit-learn does not support processing of categorical features. So we should move to numerical representation

Now, let's look at the example how to implelemt this encoding.


              123456789101112131415
            
# example of a ordinal encoding
import pandas as pd
from sklearn.preprocessing import OrdinalEncoder
# define data
data = pd.read_csv('C:/Users/User1/Desktop/РОБОТА/Data.csv')
print(data)

# define ordinal encoding
encoder = OrdinalEncoder()

# transform data
result = encoder.fit(data)
result = result.transform(data)

print(result)

It is time for an example.

Analysis

We see that here the missing values are represented by zeros(missing_values = 0), we replace them with the mean value(strategy ='mean') of the column in which the missing value is located.

Tâche

Swipe to start coding

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.

Solution

Passez à un bureau pour une pratique réelleContinuez d'où vous êtes en utilisant l'une des options ci-dessous

Tout était clair ?

Merci pour vos commentaires !

Section 1. Chapitre 3

Passez à un bureau pour une pratique réelleContinuez d'où vous êtes en utilisant l'une des options ci-dessous