{"product_id":"data-preprocessing-with-python-for-absolute-beginners-step-by-step-guide-with-hands-on-projects-and-exercises-paperback","title":"Data Preprocessing with Python for Absolute Beginners: Step-by-Step Guide with Hands-on Projects and Exercises - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eAi Publishing\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003eAre you looking for a hands-on approach to learn Data Preprocessing techniques fast?\u003cbr\u003eDo you need to start learning Python for Data Preparation from Scratch?\u003cbr\u003eThis 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.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003eFor 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.\u003cp\u003e\u003c\/p\u003eThis 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.\u003cbr\u003eWhat this book offers... \u003cbr\u003eThe 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.\u003cbr\u003eClear and Easy to Understand Solutions\u003cbr\u003eAll 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.\u003cbr\u003eTopics Covered: \u003cul\u003e\n\u003cli\u003eWhat Is Data Preparation\u003c\/li\u003e\n\u003cli\u003ePython Crash Course\u003c\/li\u003e\n\u003cli\u003eDifferent Libraries for Data Preparation\u003c\/li\u003e\n\u003cli\u003eUnderstanding Data Types\u003c\/li\u003e\n\u003cli\u003eHandling Missing Data\u003c\/li\u003e\n\u003cli\u003eEncoding Categorical Data\u003c\/li\u003e\n\u003cli\u003eData Discretization\u003c\/li\u003e\n\u003cli\u003eOutlier Handling\u003c\/li\u003e\n\u003cli\u003eFeature Scaling\u003c\/li\u003e\n\u003cli\u003eHandling Mixed and DateTime Variables\u003c\/li\u003e\n\u003cli\u003eHandling Imbalanced Datasets\u003c\/li\u003e\n\u003cli\u003eA Complete Data Preparation Pipeline\u003c\/li\u003e\n\u003cli\u003eProject 1 - Data Preparation\u003c\/li\u003e\n\u003cli\u003eProject 2 - Classification Project\u003c\/li\u003e\n\u003cli\u003eProject 3 - Regression Project\u003c\/li\u003e\n\u003c\/ul\u003eClick the BUY button and download the book now to start learning Data Preprocessing Using Python.\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 252\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.53 x 9 x 6 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e March 21, 2020\u003c\/div\u003e\n            ","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":43158017278015,"sku":"9781734790108","price":33.73,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0105\/8226\/1823\/files\/yRC2RbTAlh9781734790108.webp?v=1776986232","url":"https:\/\/dhl-adrianne.myshopify.com\/products\/data-preprocessing-with-python-for-absolute-beginners-step-by-step-guide-with-hands-on-projects-and-exercises-paperback","provider":"BBB","version":"1.0","type":"link"}