154 pages
English

Vous pourrez modifier la taille du texte de cet ouvrage

Découvre YouScribe en t'inscrivant gratuitement

Je m'inscris

Data Preprocessing with Python for Absolute Beginners , livre ebook

-

Découvre YouScribe en t'inscrivant gratuitement

Je m'inscris
Obtenez un accès à la bibliothèque pour le consulter en ligne
En savoir plus
154 pages
English

Vous pourrez modifier la taille du texte de cet ouvrage

Obtenez un accès à la bibliothèque pour le consulter en ligne
En savoir plus

Description

Are you looking for a hands-on approach to learn Data Preprocessing techniques fast?Do you need to start learning Python for Data Preparation from Scratch?This book is for you.This book is dedicated to data preparation and explains how to perform different data preparation techniques on a variety of datasets using various data preparation libraries written in the Python programming language. It is suggested that you use this book for data preparation purposes only and not for data science or machine learning.For the application of data preparation in data science and machine learning, read this book in conjunction with dedicated books on machine learning and data science.This book explains the process of data preparation using various libraries from scratch. All the codes and datasets have been provided. However, to download data preparation libraries, you will need the internet. In addition to beginners to data preparation with Python, this book can also be used as a reference manual by intermediate and experienced programmers as it contains data preparation code samples using multiple data visualization libraries.What this book offers... The book follows a very simple approach. It is divided into nine chapters. Chapter 1 introduces the basic concept of data preparation, along with the 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. A brief overview of different data types is given in Chapter 2. Chapter 3 explains how to handle missing values in the data, while the categorical encoding of numeric data is explained in Chapter 4. Data discretization is presented in Chapter 5. Chapter 6 explains the process of handline outliers, while Chapter 7 explains how to scale features in the dataset. Handling of mixed and datetime data type is explained in Chapter 8, while data balancing and resampling has been explained in Chapter 9. A full data preparation final project is also available at the end of the book. In each chapter, different types of data preparation techniques have been explained theoretically, followed by practical examples. Each chapter also contains an exercise that students can use to evaluate their understanding of the concepts explained in the chapter.Clear and Easy to Understand SolutionsAll solutions in this book are extensively tested by a group of beta readers. The solutions provided are simplified as much as possible so that they can serve as examples for you to refer to when you are learning a new skill.Topics Covered:What Is Data PreparationPython Crash CourseDifferent Libraries for Data PreparationUnderstanding Data TypesHandling Missing DataEncoding Categorical DataData DiscretizationOutlier HandlingFeature ScalingHandling Mixed and DateTime VariablesHandling Imbalanced DatasetsA Complete Data Preparation PipelineProject 1 - Data PreparationProject 2 - Classification ProjectProject 3 - Regression ProjectClick the BUY button and download the book now to start learning Data Preprocessing Using Python.

Sujets

Informations

Publié par
Date de parution 21 mars 2020
Nombre de lectures 13
EAN13 9781956591019
Langue English
Poids de l'ouvrage 2 Mo

Informations légales : prix de location à la page 0,1200€. Cette information est donnée uniquement à titre indicatif conformément à la législation en vigueur.

Extrait

© Copyright 2020 by AI Publishing
All rights reserved.
First Printing, 2020
Edited by AI Publishing
Ebook Converted and Cover by Gazler Studio
Published by AI Publishing LLC
ISBN-13: 978-1-7347901-0-8
The contents of this book may not be reproduced, duplicated, or transmitted without the direct written permission of the author. Under no circumstances will any legal responsibility or blame be held against the publisher for any reparation, damages, or monetary loss due to the information herein, either directly or indirectly.
Legal Notice:
You cannot amend, distribute, sell, use, quote, or paraphrase any part of the content within this book without the specific consent of the author.
Disclaimer Notice:
Please note the information contained within this document is for educational and entertainment purposes only. No warranties of any kind are expressed or implied. Readers acknowledge that the author is not engaging in the rendering of legal, financial, medical, or professional advice. Please consult a licensed professional before attempting any techniques outlined in this book.
By reading this document, the reader agrees that under no circumstances is the author responsible for any losses, direct or indirect, which are incurred as a result of the use of the information contained within this document, including, but not limited to, errors, omissions, or inaccuracies.
How to contact us
If you have any feedback, please let us know by sending an email to contact@aipublishing.io .
Your feedback is immensely valued, and we look forward to hearing from you. It will be beneficial for us to improve the quality of our books.
To get the Python codes and materials used in this book, please click the link below:
www.aipublishing.io/book-preprocessing-python
The order number is required.
About the Publisher
At AI Publishing Company, we have established an international learning platform specifically for young students, beginners, small enterprises, startups, and managers who are new to data sciences and artificial intelligence.
Through our interactive, coherent, and practical books and courses, we help beginners learn skills that are crucial to developing AI and data science projects.
Our courses and books range from basic introduction courses to language programming and data sciences to advanced courses for machine learning, deep learning, computer vision, big data, and much more, using programming languages like Python, R, and some data science and AI software.
AI Publishing’s core focus is to enable our learners to create and try proactive solutions for digital problems by leveraging the power of AI and data sciences to the maximum extent.
Moreover, we offer specialized assistance in the form of our free online content and eBooks, providing up-to-date and useful insight into AI practices and data science subjects, along with eliminating the doubts and misconceptions about AI and programming.
Our experts have cautiously developed our online courses and kept them concise, short, and comprehensive so that you can understand everything clearly and effectively and start practicing the applications right away.
We also offer consultancy and corporate training in AI and data sciences for enterprises so that their staff can navigate through the workflow efficiently.
With AI Publishing, you can always stay closer to the innovative world of AI and data sciences.
If you are eager to learn the A to Z of AI and data sciences but have no clue where to start, AI Publishing is the finest place to go.
Please contact us by email at: contact@aipublishing.io .
AI Publishing Is Searching for Authors Like You
Interested in becoming an author for AI Publishing? Please contact us at author@aipublishing.io .
We are working with developers and AI tech professionals just like you, to help them share their insights with the global AI and Data Science lovers. You can share all your knowledge about hot topics in AI and Data Science.
Download the Color Images
We request you to download the PDF file containing the color images of the screenshots/diagrams used in this book here:
www.aipublishing.io/book-preprocessing-python
The order number is required.
Get in Touch with Us
Feedback from our readers is always welcome.
For general feedback, please send us an email
at contact@aipublishing.io and
mention the book title in the subject line.
Although we have taken extraordinary care to ensure the accuracy of our content, errors do occur. If you have found an error in this book, we would be grateful if you could report this to us as soon as you can.
If you are interested in becoming an AI Publishing author and if you have expertise in a topic and you are interested in either writing or contributing to a book, please send us an email at author@aipublishing.io .
Warning
In Python, indentation is very important. Python indentation is a way of telling a Python interpreter that the group of statements belongs to a particular code block. After each loop or if-condition, be sure to pay close attention to the intent.
Example

To avoid problems during execution, we advise you to download the codes available on Github by requesting access from the link below. Please have your order number ready for access:
www.aipublishing.io/book-preprocessing-python
Table of Contents
How to contact us
About the Publisher
AI Publishing Is Searching for Authors Like You
Download the Color Images
Get in Touch with Us
Preface
About the Author
Chapter 1: Introduction
1.1. What is Data Preprocessing?
1.2. Environment Setup
1.2.1. Windows Setup
1.2.2. Mac Setup
1.2.3. Linux Setup
1.3. Python Crash Course
1.3.1. Writing Your First Program
1.3.2. Python Variables and Data Types
1.3.3. Python Operators
1.3.4. Conditional Statements
1.3.5. Iteration Statements
1.3.6. Functions
1.3.7. Objects and Classes
1.4. Different Libraries for Data Preprocessing
1.4.1. NumPy
1.4.2. Scikit Learn
1.4.3. Matplotlib
1.4.4. Seaborn
1.4.5. Pandas
Exercise 1.1
Exercise 1.2
Chapter 2: Understanding Data Types
2.1. Introduction
2.1.1. What Is a Variable?
2.1.2. Data Types
2.2. Numerical Data
2.2.1. Discrete Data
2.2.2. Continuous Data
2.2.3. Binary Data
2.3. Categorical Data
2.3.1. Ordinal Data
2.3.2. Nominal Data
2.4. Date and Time Data
2.5. Mixed Data Type
2.6. Missing Values
2.6.1. Causes of Missing Data
2.6.2. Disadvantages of Missing Data
2.6.3. Mechanism Behind Missing Values
2.7. Cardinality in Categorical Data
2.8. Probability Distribution
2.9. Outliers
Exercise 2.1
Chapter 3: Handling Missing Data
3.1. Introduction
3.2. Complete Case Analysis
3.3. Handling Missing Numerical Data
3.3.1. Mean or Median Imputation
3.3.2. End of Distribution Imputation
3.3.3. Arbitrary Value Imputation
3.4. Handling Missing Categorical Data
3.4.1. Frequent Category Imputation
3.4.2. Missing Category Imputation
Exercise 3.1
Exercise 3.2
Chapter 4: Encoding Categorical Data
4.1. Introduction
4.2. One Hot Encoding
4.3. Label Encoding
4.4. Frequency Encoding
4.5. Ordinal Encoding
4.6. Mean Encoding
Exercise 4.1
Exercise 4.2
Chapter 5: Data Discretization
5.1. Introduction
5.2. Equal Width Discretization
5.3. Equal Frequency Discretization
5.4. K-Means Discretization
5.5. Decision Tree Discretization
5.6. Custom Discretization
Exercise 5.1
Exercise 5.2
Chapter 6: Outlier Handling
6.1. Introduction
6.2. Outlier Trimming
6.3. Outlier Capping Using IQR
6.4. Outlier Capping Using Mean and Std
6.5. Outlier Capping Using Quantiles
6.6. Outlier Capping using Custom Values
Exercise 6.1
Exercise 6.2
Chapter 7: Feature Scaling
7.1. Introduction
7.2. Standardization
7.3. Min/Max Scaling
7.4. Mean Normalization
7.5. Maximum Absolute Scaling
7.6. Median and Quantile Scaling
7.7. Vector Unit Length Scaling
Exercise 7.1
Exercise 7.2
Chapter 8: Handling Mixed and DateTime Variables
8.1. Introduction
8.2. Handling Mixed Values
8.3. Handling Date Data Type
8.4. Handling Time Data Type
Exercise 8.1
Exercise 8.2
Chapter 9: Handling Imbalanced Datasets
9.1. Introduction
9.2. Example of Imbalanced Dataset
9.3. Down Sampling
9.4. Up Sampling
9.5. SMOTE Up Sampling
Exercise 9.1
Final Project – A Complete Data Preparation Pipeline
1.1. Introduction
1.2. Data Preparation
1.3. Classification Project
1.4. Regression Project
From the Same Publisher
Exercise Solutions
Exercise 2.1
Exercise 3.1
Exercise 3.2
Exercise 4.1
Exercise 4.2
Exercise 5.1
Exercise 5.2
Exercise 6.1
Exercise 6.2
Exercise 7.1
Exercise 7.2
Exercise 8.1
Exercise 8.2
Exercise 9.1
Preface
§ Book Approach
The book follows a very simple approach. It is divided into nine chapters. Chapter 1 introduces the basic concept of data preparation, along with the 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. A brief overview of different data types is given in Chapter 2. Chapter 3 explains how to handle missing values in the data, while the categorical encoding of numeric data is explained in Chapter 4. Data discretization is presented in Chapter 5. Chapter 6 explains the process of handline outliers, while Chapter 7 explains how to scale features in the dataset. Handling of mixed and datetime data type is explained in Chapter 8, while data balancing and resampling has been explained in Chapter 9. A full data preparation final project is also available at the end of the book.
In each chapter, different types of data preparation techniques have been explained theoretically, followed by practical examples. Each chapter also contains an exercise that students can use to evaluate their understanding of the concepts explained in the chapter. The Python notebook for each chapter is provided in the reso

  • Univers Univers
  • Ebooks Ebooks
  • Livres audio Livres audio
  • Presse Presse
  • Podcasts Podcasts
  • BD BD
  • Documents Documents
Alternate Text