This book is dedicated to data preparation and explains how to perform different data preparation techniques on various datasets using different data preparation libraries written in the Python programming language.

Key Features

A crash course in Python to fill any gaps in prerequisite knowledge and a solid foundation on which to build your new skills

A complete data preparation pipeline for your guided practice

Three real-world projects covering each major task to cement your learned skills in data preparation, classification, and regression

Book Description

The book follows a straightforward approach. It is divided into nine chapters. Chapter 1 introduces the basic concept of data preparation and installation steps for the software that we will need to perform data preparation in this book. Chapter 1 also contains a crash course on Python, followed by a brief overview of different data types in Chapter 2. You will then learn how to handle missing values in the data, while the categorical encoding of numeric data is explained in Chapter 4.

The second half of the course presents data discretization and describes the handling of outliers' process. Chapter 7 demonstrates how to scale features in the dataset. Subsequent chapters teach you to handle mixed and DateTime data type, balance data, and practice resampling. A full data preparation final project is also available at the end of the book.

Different types of data preprocessing techniques have been explained theoretically, followed by practical examples in each chapter. Each chapter also contains an exercise that students can use to evaluate their understanding of the chapter's concepts. By the end of this course, you will have built a solid working knowledge in data preparation—the first steps to any data science or machine learning career and an essential skillset for any aspiring developer.

The code bundle for this course is available at https://www.aispublishing.net/book-data-preprocessing

What you will learn

Explore different libraries for data preparation

Understand data types

Handle missing data

Encode categorical data

Discretize data

Learn to handle outliers

Practice feature scaling

Handle mixed and DateTime variables and imbalanced datasets

Employ your new skills to complete projects in data preparation, classification, and regression

Who this book is for

In addition to beginners in data preparation with Python, this book can also be used as a reference manual by intermediate and experienced programmers. It contains data preprocessing code samples using multiple data visualization libraries.