Data Preprocessing

Data Preprocessing

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1. Brief Introduction

Different types of data? How to work with them? If your eyes are wide open, don't worry, let's start with a brief overview of the pandas library and learn how to work with it in the future.

Data Processing Methods

Dataset: Test and Training

Deleting an "Extra" Data

Changing the Data Type

2. Processing Quantitative Data

This chapter discusses in detail how to work with quantitative data, what methods it is processed with, how data scaling and normalization differ, and much more.

Data Scaling vs Data Normalization

Removing Outliers

Removing Missing Values

Data Augmentation: Synthetic Data

3. Processing Categorical Data

Is categorical data as simple as you think it is? Find out what is the complexity of processing and working with it.

Methods for Encoding the Categorical Data

One-Hot Encoding

Ordinal Encoding

Label Encoding of the Target Variable

4. Time Series Data Processing

Time series data processing is the process of handling, analyzing, and preparing data that is presented as a sequence of temporally ordered values. Find out what steps it includes in this section.

Data Type Conversion

Train/Test Split & Cross Validation

5. Feature Engineering

Did you know that you can extract even more values from your data and create more informative features? In this section, you will learn how to work with feature engineering.

Feature Extraction from Text

Feature Extraction from Images

Feature Extraction from Time Series

6. Moving on to Tasks

You have reached the end of this course. Let's test your knowledge! There are 3 tasks for you to solve.

Course Content

Data Preprocessing

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

Methods for Encoding the Categorical Data

Categorical data is a type of data that represents qualitative or descriptive characteristics. It is often non-numeric. These can be car brands, professions, education level, etc. But then, what is the difference between plain text data and categorical data? The main difference between categorical data and text data - is that categorical data is a structured type of data with discrete categories, while text data - is an unstructured type of data that requires additional preprocessing steps to extract relevant information. That is why, for example, the names of people in a dataset with user resumes are not categorical data but text data.

First of all, let's find out why we need to encode categorical data. Most machine learning algorithms require numeric input data to be able to perform their computations, so categorical data needs to be transformed into a numerical representation before it can be used.

There are many data encoding methods: label encoding, one-hot encoding, binary encoding, target encoding, and others, the differences between which we will discuss in the following chapters.

You can see the difference between one-hot encoding and label encoding in the images below:

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Section 3. Chapter 1

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