Hands On Data Preprocessing In Python
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Hands-On Data Preprocessing in Python
Get your raw data cleaned up and ready for processing to design better data analytic solutions Key FeaturesDevelop the skills to perform data cleaning, data integration, data reduction, and data transformationMake the most of your raw data with powerful data transformation and massaging techniquesPerform thorough data cleaning, including dealing with missing values and outliersBook Description Hands-On Data Preprocessing is a primer on the best data cleaning and preprocessing techniques, written by an expert who's developed college-level courses on data preprocessing and related subjects. With this book, you'll be equipped with the optimum data preprocessing techniques from multiple perspectives, ensuring that you get the best possible insights from your data. You'll learn about different technical and analytical aspects of data preprocessing – data collection, data cleaning, data integration, data reduction, and data transformation – and get to grips with implementing them using the open source Python programming environment. The hands-on examples and easy-to-follow chapters will help you gain a comprehensive articulation of data preprocessing, its whys and hows, and identify opportunities where data analytics could lead to more effective decision making. As you progress through the chapters, you'll also understand the role of data management systems and technologies for effective analytics and how to use APIs to pull data. By the end of this Python data preprocessing book, you'll be able to use Python to read, manipulate, and analyze data; perform data cleaning, integration, reduction, and transformation techniques, and handle outliers or missing values to effectively prepare data for analytic tools. What you will learnUse Python to perform analytics functions on your dataUnderstand the role of databases and how to effectively pull data from databasesPerform data preprocessing steps defined by your analytics goalsRecognize and resolve data integration challengesIdentify the need for data reduction and execute itDetect opportunities to improve analytics with data transformationWho this book is for This book is for junior and senior data analysts, business intelligence professionals, engineering undergraduates, and data enthusiasts looking to perform preprocessing and data cleaning on large amounts of data. You don't need any prior experience with data preprocessing to get started with this book. However, basic programming skills, such as working with variables, conditionals, and loops, along with beginner-level knowledge of Python and simple analytics experience, are a prerequisite.
Machine Learning With Python: A Hands-on Introduction
Author: Siddharth Savyasachi Malu & Sanjay Kumar Pandey
language: en
Publisher: S. Chand Publishing
Release Date:
Embark on an exciting journey into the world of Machine Learning with "Machine Learning with Python: A Hands-on Introduction" - your guide to demystifying complex concepts and turning data into actionable insights. Picture yourself stepping into the fascinating realm of data exploration and visualisation. It's like embarking on a thrilling adventure, where you'll learn the language of statistics and uncover hidden patterns in your data, preparing you for the challenges ahead. As you venture further, you'll be introduced to the heroes of supervised machine learning - from the dependable Linear Regression to the versatile Tree-Based Methods. Each chapter is designed to build your confidence, like mastering a new skill that empowers you to make sense of the world around you step by step. But wait, there's more! Unit III invites you to meet the geniuses of the machine learning world Neural Networks. Imagine diving into the inner workings of these digital brains, understanding how they learn and adapt, just like we humans do. As the journey progresses, you'll encounter unsupervised learning, where data reveals its secrets without guidance. It's like exploring uncharted territory, where every discovery brings you closer to understanding the mysteries of your data. And fear not, weary traveller, for our appendices are like trusty companions, ready to assist you on your quest. From Python basics to advanced data wrangling and visualisation techniques, they're here to lend a helping hand whenever you need them. So, whether you're a curious beginner or a seasoned explorer, "Machine Learning with Python: A Hands-on Introduction" promises an adventure like no other. Join us, and together, let's unlock the power of data and embark on a thrilling journey of discovery and innovation.
Data Preprocessing with Python for Absolute Beginners
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 Preparation Python Crash Course Different Libraries for Data Preparation Understanding Data Types Handling Missing Data Encoding Categorical Data Data Discretization Outlier Handling Feature Scaling Handling Mixed and DateTime Variables Handling Imbalanced Datasets A Complete Data Preparation Pipeline Project 1 - Data Preparation Project 2 - Classification Project Project 3 - Regression Project Click the BUY button and download the book now to start learning Data Preprocessing Using Python.