Hands On Data Preprocessing In Python
DOWNLOAD
Download Hands On Data Preprocessing In Python PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Hands On Data Preprocessing In Python book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Hands On Data Preprocessing In Python
DOWNLOAD
Author : Roy Jafari
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-01-21
Hands On Data Preprocessing In Python written by Roy Jafari and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-21 with Computers categories.
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.
Data Preprocessing With Python For Absolute Beginners
DOWNLOAD
Author : Ai Publishing
language : en
Publisher:
Release Date : 2020-03-21
Data Preprocessing With Python For Absolute Beginners written by Ai Publishing and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-21 with categories.
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.
Hands On Data Science For Biologists Using Python
DOWNLOAD
Author : Yasha Hasija
language : en
Publisher: CRC Press
Release Date : 2021-04-08
Hands On Data Science For Biologists Using Python written by Yasha Hasija and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-08 with Computers categories.
Hands-on Data Science for Biologists using Python has been conceptualized to address the massive data handling needs of modern-day biologists. With the advent of high throughput technologies and consequent availability of omics data, biological science has become a data-intensive field. This hands-on textbook has been written with the inception of easing data analysis by providing an interactive, problem-based instructional approach in Python programming language. The book starts with an introduction to Python and steadily delves into scrupulous techniques of data handling, preprocessing, and visualization. The book concludes with machine learning algorithms and their applications in biological data science. Each topic has an intuitive explanation of concepts and is accompanied with biological examples. Features of this book: The book contains standard templates for data analysis using Python, suitable for beginners as well as advanced learners. This book shows working implementations of data handling and machine learning algorithms using real-life biological datasets and problems, such as gene expression analysis; disease prediction; image recognition; SNP association with phenotypes and diseases. Considering the importance of visualization for data interpretation, especially in biological systems, there is a dedicated chapter for the ease of data visualization and plotting. Every chapter is designed to be interactive and is accompanied with Jupyter notebook to prompt readers to practice in their local systems. Other avant-garde component of the book is the inclusion of a machine learning project, wherein various machine learning algorithms are applied for the identification of genes associated with age-related disorders. A systematic understanding of data analysis steps has always been an important element for biological research. This book is a readily accessible resource that can be used as a handbook for data analysis, as well as a platter of standard code templates for building models.
Python Data Analysis
DOWNLOAD
Author : Avinash Navlani
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-02-05
Python Data Analysis written by Avinash Navlani and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-05 with Computers categories.
Understand data analysis pipelines using machine learning algorithms and techniques with this practical guide Key FeaturesPrepare and clean your data to use it for exploratory analysis, data manipulation, and data wranglingDiscover supervised, unsupervised, probabilistic, and Bayesian machine learning methodsGet to grips with graph processing and sentiment analysisBook Description Data analysis enables you to generate value from small and big data by discovering new patterns and trends, and Python is one of the most popular tools for analyzing a wide variety of data. With this book, you'll get up and running using Python for data analysis by exploring the different phases and methodologies used in data analysis and learning how to use modern libraries from the Python ecosystem to create efficient data pipelines. Starting with the essential statistical and data analysis fundamentals using Python, you'll perform complex data analysis and modeling, data manipulation, data cleaning, and data visualization using easy-to-follow examples. You'll then understand how to conduct time series analysis and signal processing using ARMA models. As you advance, you'll get to grips with smart processing and data analytics using machine learning algorithms such as regression, classification, Principal Component Analysis (PCA), and clustering. In the concluding chapters, you'll work on real-world examples to analyze textual and image data using natural language processing (NLP) and image analytics techniques, respectively. Finally, the book will demonstrate parallel computing using Dask. By the end of this data analysis book, you'll be equipped with the skills you need to prepare data for analysis and create meaningful data visualizations for forecasting values from data. What you will learnExplore data science and its various process modelsPerform data manipulation using NumPy and pandas for aggregating, cleaning, and handling missing valuesCreate interactive visualizations using Matplotlib, Seaborn, and BokehRetrieve, process, and store data in a wide range of formatsUnderstand data preprocessing and feature engineering using pandas and scikit-learnPerform time series analysis and signal processing using sunspot cycle dataAnalyze textual data and image data to perform advanced analysisGet up to speed with parallel computing using DaskWho this book is for This book is for data analysts, business analysts, statisticians, and data scientists looking to learn how to use Python for data analysis. Students and academic faculties will also find this book useful for learning and teaching Python data analysis using a hands-on approach. A basic understanding of math and working knowledge of the Python programming language will help you get started with this book.
Mitigating Bias In Machine Learning
DOWNLOAD
Author : Carlotta A. Berry
language : en
Publisher: McGraw Hill Professional
Release Date : 2024-10-18
Mitigating Bias In Machine Learning written by Carlotta A. Berry and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-18 with Technology & Engineering categories.
This practical guide shows, step by step, how to use machine learning to carry out actionable decisions that do not discriminate based on numerous human factors, including ethnicity and gender. The authors examine the many kinds of bias that occur in the field today and provide mitigation strategies that are ready to deploy across a wide range of technologies, applications, and industries. Edited by engineering and computing experts, Mitigating Bias in Machine Learning includes contributions from recognized scholars and professionals working across different artificial intelligence sectors. Each chapter addresses a different topic and real-world case studies are featured throughout that highlight discriminatory machine learning practices and clearly show how they were reduced. Mitigating Bias in Machine Learning addresses: Ethical and Societal Implications of Machine Learning Social Media and Health Information Dissemination Comparative Case Study of Fairness Toolkits Bias Mitigation in Hate Speech Detection Unintended Systematic Biases in Natural Language Processing Combating Bias in Large Language Models Recognizing Bias in Medical Machine Learning and AI Models Machine Learning Bias in Healthcare Achieving Systemic Equity in Socioecological Systems Community Engagement for Machine Learning
Astronomical Data Analysis Software And Systems Xiii
DOWNLOAD
Author : F. Ochsenbein
language : en
Publisher:
Release Date : 2004
Astronomical Data Analysis Software And Systems Xiii written by F. Ochsenbein and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Computers categories.
Data Preprocessing With Python For Absolute Beginners
DOWNLOAD
Author : A. I. Sciences OU
language : en
Publisher:
Release Date : 2021-03-25
Data Preprocessing With Python For Absolute Beginners written by A. I. Sciences OU and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-25 with categories.
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 regressionBook DescriptionThe 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-preprocessingWhat 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 regressionWho this book is forIn 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.
Python For Programmers
DOWNLOAD
Author : Paul Deitel
language : en
Publisher: Prentice Hall
Release Date : 2019-03-15
Python For Programmers written by Paul Deitel and has been published by Prentice Hall this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-15 with Computers categories.
The professional programmer’s Deitel® guide to Python® with introductory artificial intelligence case studies Written for programmers with a background in another high-level language, Python for Programmers uses hands-on instruction to teach today’s most compelling, leading-edge computing technologies and programming in Python–one of the world’s most popular and fastest-growing languages. Please read the Table of Contents diagram inside the front cover and the Preface for more details. In the context of 500+, real-world examples ranging from individual snippets to 40 large scripts and full implementation case studies, you’ll use the interactive IPython interpreter with code in Jupyter Notebooks to quickly master the latest Python coding idioms. After covering Python Chapters 1-5 and a few key parts of Chapters 6-7, you’ll be able to handle significant portions of the hands-on introductory AI case studies in Chapters 11-16, which are loaded with cool, powerful, contemporary examples. These include natural language processing, data mining Twitter® for sentiment analysis, cognitive computing with IBM® WatsonTM, supervised machine learning with classification and regression, unsupervised machine learning with clustering, computer vision through deep learning and convolutional neural networks, deep learning with recurrent neural networks, big data with Hadoop®, SparkTM and NoSQL databases, the Internet of Things and more. You’ll also work directly or indirectly with cloud-based services, including Twitter, Google TranslateTM, IBM Watson, Microsoft® Azure®, OpenMapQuest, PubNub and more. Features 500+ hands-on, real-world, live-code examples from snippets to case studies IPython + code in Jupyter® Notebooks Library-focused: Uses Python Standard Library and data science libraries to accomplish significant tasks with minimal code Rich Python coverage: Control statements, functions, strings, files, JSON serialization, CSV, exceptions Procedural, functional-style and object-oriented programming Collections: Lists, tuples, dictionaries, sets, NumPy arrays, pandas Series & DataFrames Static, dynamic and interactive visualizations Data experiences with real-world datasets and data sources Intro to Data Science sections: AI, basic stats, simulation, animation, random variables, data wrangling, regression AI, big data and cloud data science case studies: NLP, data mining Twitter®, IBM® WatsonTM, machine learning, deep learning, computer vision, Hadoop®, SparkTM, NoSQL, IoT Open-source libraries: NumPy, pandas, Matplotlib, Seaborn, Folium, SciPy, NLTK, TextBlob, spaCy, Textatistic, Tweepy, scikit-learn®, Keras and more Accompanying code examples are available here: http://ptgmedia.pearsoncmg.com/imprint_downloads/informit/bookreg/9780135224335/9780135224335_examples.zip. Register your product for convenient access to downloads, updates, and/or corrections as they become available. See inside book for more information.
Hands On Data Structures And Algorithms With Python
DOWNLOAD
Author : Dr. Basant Agarwal
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-10-31
Hands On Data Structures And Algorithms With Python written by Dr. Basant Agarwal and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-31 with Computers categories.
Learn to implement complex data structures and algorithms using Python Key FeaturesUnderstand the analysis and design of fundamental Python data structuresExplore advanced Python concepts such as Big O notation and dynamic programmingLearn functional and reactive implementations of traditional data structuresBook Description Data structures allow you to store and organize data efficiently. They are critical to any problem, provide a complete solution, and act like reusable code. Hands-On Data Structures and Algorithms with Python teaches you the essential Python data structures and the most common algorithms for building easy and maintainable applications. This book helps you to understand the power of linked lists, double linked lists, and circular linked lists. You will learn to create complex data structures, such as graphs, stacks, and queues. As you make your way through the chapters, you will explore the application of binary searches and binary search trees, along with learning common techniques and structures used in tasks such as preprocessing, modeling, and transforming data. In the concluding chapters, you will get to grips with organizing your code in a manageable, consistent, and extendable way. You will also study how to bubble sort, selection sort, insertion sort, and merge sort algorithms in detail. By the end of the book, you will have learned how to build components that are easy to understand, debug, and use in different applications. You will get insights into Python implementation of all the important and relevant algorithms. What you will learnUnderstand object representation, attribute binding, and data encapsulationGain a solid understanding of Python data structures using algorithmsStudy algorithms using examples with pictorial representationLearn complex algorithms through easy explanation, implementing PythonBuild sophisticated and efficient data applications in PythonUnderstand common programming algorithms used in Python data scienceWrite efficient and robust code in Python 3.7Who this book is for This book is for developers who want to learn data structures and algorithms in Python to write complex and flexible programs. Basic Python programming knowledge is expected.
Document Warehousing And Text Mining
DOWNLOAD
Author : Dan Sullivan
language : en
Publisher:
Release Date : 2001-03-07
Document Warehousing And Text Mining written by Dan Sullivan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-03-07 with Business & Economics categories.
Although data warehousing is essential, the real payoff is in mining this text to provide timely and accurate information to decision makers. The goals of text mining are similar to those of data mining, but the techniques differ. This book explains these text mining techniques.