Course Content

Data Preprocessing

1. Brief Introduction

Data Types Data Processing Methods Dataset: Test and Training Deleting an "Extra" Data Changing the Data Type

2. Processing Quantitative Data

Data Scaling Data Scaling vs Data Normalization Removing Outliers Removing Missing Values Data Augmentation: Synthetic Data

3. Processing Categorical Data

Methods for Encoding the Categorical Data One-Hot Encoding Ordinal Encoding Label Encoding of the Target Variable Challenge

4. Time Series Data Processing

Data Type Conversion Data Cleaning Stationarity Denoising Train/Test Split & Cross Validation Challenge

5. Feature Engineering

Technique Idea Realization Feature Extraction from Text Feature Extraction from Images Feature Extraction from Time Series Challenge

6. Moving on to Tasks

Challenge 1 Challenge 2 Challenge 3

Label Encoding of the Target Variable

Let's go straight to the main thing - label encoding implements everything the same as ordinal encoder, but:

Methods work with different data dimensions;
The order of the categories is not important for label encoding.

How to use this method in Python:


              1234567891011121314
            
from sklearn.preprocessing import LabelEncoder
import pandas as pd

# Simple categorical variable
fruits = pd.Series(['apple', 'orange', 'banana', 'banana', 'apple', 'orange', 'banana'])

# Create label encoder object
le = LabelEncoder()

# Fit and transform the categorical variable using label encoding
fruits_encoded = le.fit_transform(fruits)

# Print the encoded values
print(fruits_encoded)

Task

Read the dataset 'salary_and_gender.csv' and encode the output column 'Gender' with label encoding.

Task

Read the dataset 'salary_and_gender.csv' and encode the output column 'Gender' with label encoding.

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

Everything was clear?

Section 3. Chapter 4

Label Encoding of the Target Variable

Let's go straight to the main thing - label encoding implements everything the same as ordinal encoder, but:

Methods work with different data dimensions;
The order of the categories is not important for label encoding.

How to use this method in Python:


              1234567891011121314
            
from sklearn.preprocessing import LabelEncoder
import pandas as pd

# Simple categorical variable
fruits = pd.Series(['apple', 'orange', 'banana', 'banana', 'apple', 'orange', 'banana'])

# Create label encoder object
le = LabelEncoder()

# Fit and transform the categorical variable using label encoding
fruits_encoded = le.fit_transform(fruits)

# Print the encoded values
print(fruits_encoded)

Task

Read the dataset 'salary_and_gender.csv' and encode the output column 'Gender' with label encoding.

Task

Read the dataset 'salary_and_gender.csv' and encode the output column 'Gender' with label encoding.

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

Everything was clear?

Section 3. Chapter 4

Label Encoding of the Target Variable

Let's go straight to the main thing - label encoding implements everything the same as ordinal encoder, but:

Methods work with different data dimensions;
The order of the categories is not important for label encoding.

How to use this method in Python:


              1234567891011121314
            
from sklearn.preprocessing import LabelEncoder
import pandas as pd

# Simple categorical variable
fruits = pd.Series(['apple', 'orange', 'banana', 'banana', 'apple', 'orange', 'banana'])

# Create label encoder object
le = LabelEncoder()

# Fit and transform the categorical variable using label encoding
fruits_encoded = le.fit_transform(fruits)

# Print the encoded values
print(fruits_encoded)

Task

Read the dataset 'salary_and_gender.csv' and encode the output column 'Gender' with label encoding.

Task

Read the dataset 'salary_and_gender.csv' and encode the output column 'Gender' with label encoding.

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

Everything was clear?

Let's go straight to the main thing - label encoding implements everything the same as ordinal encoder, but:

Methods work with different data dimensions;
The order of the categories is not important for label encoding.

How to use this method in Python:


              1234567891011121314
            
from sklearn.preprocessing import LabelEncoder
import pandas as pd

# Simple categorical variable
fruits = pd.Series(['apple', 'orange', 'banana', 'banana', 'apple', 'orange', 'banana'])

# Create label encoder object
le = LabelEncoder()

# Fit and transform the categorical variable using label encoding
fruits_encoded = le.fit_transform(fruits)

# Print the encoded values
print(fruits_encoded)

Task

Read the dataset 'salary_and_gender.csv' and encode the output column 'Gender' with label encoding.

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

Section 3. Chapter 4

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