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

One-Hot Encoding

So, let's start to understand when and what encoding methods are best to use.

One-hot encoding is generally better to use when the categorical variable has no natural ordering or hierarchy between the categories and when the number of unique categories is relatively small. It is commonly used for nominal categorical data, where the categories have no inherent order or relationship between them.

Take a look at some examples of nominal categorical data:

Colors: red, blue, green, yellow, etc.;
Countries: USA, Canada, Mexico, Japan, etc.;
Different pets: dog, cat, bird, fish, etc.;
Genres of music: pop, rock, hip hop, country, etc.;
Marital status: single, married, divorced, widowed, etc..

The basic idea behind one-hot encoding is to create a binary (0/1) variable for each category in the categorical variable.

We can perform one-hot encoding using the pd.get_dummies() method, which creates 3 new binary columns for each of the three unique color values. The resulting dataset shows the binary representation of each color value:


              12345678910
            
import pandas as pd

# Create a sample dataset with categorical data
dataset = pd.DataFrame({'color': ['red', 'green', 'blue', 'red', 'blue']})

# Perform one-hot encoding
one_hot_encoded = pd.get_dummies(dataset['color'])

# Display the one-hot encoded dataframe
print(one_hot_encoded)

Task

Use the one-hot encoding method on the 'cars.csv' dataset.

Task

Use the one-hot encoding method on the 'cars.csv' dataset.

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

Everything was clear?

Section 3. Chapter 2

One-Hot Encoding

So, let's start to understand when and what encoding methods are best to use.

Take a look at some examples of nominal categorical data:

Colors: red, blue, green, yellow, etc.;
Countries: USA, Canada, Mexico, Japan, etc.;
Different pets: dog, cat, bird, fish, etc.;
Genres of music: pop, rock, hip hop, country, etc.;
Marital status: single, married, divorced, widowed, etc..

The basic idea behind one-hot encoding is to create a binary (0/1) variable for each category in the categorical variable.


              12345678910
            
import pandas as pd

# Create a sample dataset with categorical data
dataset = pd.DataFrame({'color': ['red', 'green', 'blue', 'red', 'blue']})

# Perform one-hot encoding
one_hot_encoded = pd.get_dummies(dataset['color'])

# Display the one-hot encoded dataframe
print(one_hot_encoded)

Task

Use the one-hot encoding method on the 'cars.csv' dataset.

Task

Use the one-hot encoding method on the 'cars.csv' dataset.

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

Everything was clear?

Section 3. Chapter 2

One-Hot Encoding

So, let's start to understand when and what encoding methods are best to use.

Take a look at some examples of nominal categorical data:

Colors: red, blue, green, yellow, etc.;
Countries: USA, Canada, Mexico, Japan, etc.;
Different pets: dog, cat, bird, fish, etc.;
Genres of music: pop, rock, hip hop, country, etc.;
Marital status: single, married, divorced, widowed, etc..

The basic idea behind one-hot encoding is to create a binary (0/1) variable for each category in the categorical variable.


              12345678910
            
import pandas as pd

# Create a sample dataset with categorical data
dataset = pd.DataFrame({'color': ['red', 'green', 'blue', 'red', 'blue']})

# Perform one-hot encoding
one_hot_encoded = pd.get_dummies(dataset['color'])

# Display the one-hot encoded dataframe
print(one_hot_encoded)

Task

Use the one-hot encoding method on the 'cars.csv' dataset.

Task

Use the one-hot encoding method on the 'cars.csv' dataset.

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

Everything was clear?

So, let's start to understand when and what encoding methods are best to use.

Take a look at some examples of nominal categorical data:

Colors: red, blue, green, yellow, etc.;
Countries: USA, Canada, Mexico, Japan, etc.;
Different pets: dog, cat, bird, fish, etc.;
Genres of music: pop, rock, hip hop, country, etc.;
Marital status: single, married, divorced, widowed, etc..

The basic idea behind one-hot encoding is to create a binary (0/1) variable for each category in the categorical variable.


              12345678910
            
import pandas as pd

# Create a sample dataset with categorical data
dataset = pd.DataFrame({'color': ['red', 'green', 'blue', 'red', 'blue']})

# Perform one-hot encoding
one_hot_encoded = pd.get_dummies(dataset['color'])

# Display the one-hot encoded dataframe
print(one_hot_encoded)

Task

Use the one-hot encoding method on the 'cars.csv' dataset.

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

Section 3. Chapter 2

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