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Learning Data Mining with Python

Author : Robert Layton

Publisher : Packt Publishing Ltd

Page : 344 pages

File Size : 40,34 MB

Release : 2015-07-29

Category : Computers

ISBN : 1784391204

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Book Description

The next step in the information age is to gain insights from the deluge of data coming our way. Data mining provides a way of finding this insight, and Python is one of the most popular languages for data mining, providing both power and flexibility in analysis. This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. Next, we move on to more complex data types including text, images, and graphs. In every chapter, we create models that solve real-world problems. There is a rich and varied set of libraries available in Python for data mining. This book covers a large number, including the IPython Notebook, pandas, scikit-learn and NLTK. Each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will gain a large insight into using Python for data mining, with a good knowledge and understanding of the algorithms and implementations.



Learning Data Mining with Python

Author : Robert Layton

Publisher :

Page : 0 pages

File Size : 47,88 MB

Release : 2015

Category : Data mining

ISBN : 9781784396053

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Book Description

About This Book Learn data mining in practical terms, using a wide variety of libraries and techniques Learn how to find, manipulate, and analyze data using Python Step-by-step instructions on creating real-world applications of data mining techniques Who This Book Is For If you are a programmer who wants to get started with data mining, then this book is for you. What You Will Learn Apply data mining concepts to real-world problems Predict the outcome of sports matches based on past results Determine the author of a document based on their writing style Use APIs to download datasets from social media and other online services Find and extract good features from difficult datasets Create models that solve real-world problems Design and develop data mining applications using a variety of datasets Set up reproducible experiments and generate robust results Recommend movies, online celebrities, and news articles based on personal preferences Compute on big data, including real-time data from the Internet In Detail The next step in the information age is to gain insights from the deluge of data coming our way. Data mining provides a way of finding this insight, and Python is one of the most popular languages for data mining, providing both power and flexibility in analysis. This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. Next, we move on to more complex data types including text, images, and graphs. In every chapter, we create models that solve real-world problems. There is a rich and varied set of libraries available in Python for data mining. This book covers a large number, including the IPython Notebook, pandas, scikit-learn and NLTK. Each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will gain a large insight into using Python for data mining, with a good knowledge and understanding of the algorithms and implementations.



Hands-On Data Science and Python Machine Learning

Author : Frank Kane

Publisher : Packt Publishing Ltd

Page : 415 pages

File Size : 15,88 MB

Release : 2017-07-31

Category : Computers

ISBN : 1787280225

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Book Description

This book covers the fundamentals of machine learning with Python in a concise and dynamic manner. It covers data mining and large-scale machine learning using Apache Spark. About This Book Take your first steps in the world of data science by understanding the tools and techniques of data analysis Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods Learn how to use Apache Spark for processing Big Data efficiently Who This Book Is For If you are a budding data scientist or a data analyst who wants to analyze and gain actionable insights from data using Python, this book is for you. Programmers with some experience in Python who want to enter the lucrative world of Data Science will also find this book to be very useful, but you don't need to be an expert Python coder or mathematician to get the most from this book. What You Will Learn Learn how to clean your data and ready it for analysis Implement the popular clustering and regression methods in Python Train efficient machine learning models using decision trees and random forests Visualize the results of your analysis using Python's Matplotlib library Use Apache Spark's MLlib package to perform machine learning on large datasets In Detail Join Frank Kane, who worked on Amazon and IMDb's machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank's successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis. Style and approach This comprehensive book is a perfect blend of theory and hands-on code examples in Python which can be used for your reference at any time.



Python Data Mining Quick Start Guide

Author : Nathan Greeneltch

Publisher : Packt Publishing Ltd

Page : 181 pages

File Size : 48,52 MB

Release : 2019-04-25

Category : Computers

ISBN : 1789806402

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Book Description

Explore the different data mining techniques using the libraries and packages offered by Python Key FeaturesGrasp the basics of data loading, cleaning, analysis, and visualizationUse the popular Python libraries such as NumPy, pandas, matplotlib, and scikit-learn for data miningYour one-stop guide to build efficient data mining pipelines without going into too much theoryBook Description Data mining is a necessary and predictable response to the dawn of the information age. It is typically defined as the pattern and/ or trend discovery phase in the data mining pipeline, and Python is a popular tool for performing these tasks as it offers a wide variety of tools for data mining. This book will serve as a quick introduction to the concept of data mining and putting it to practical use with the help of popular Python packages and libraries. You will get a hands-on demonstration of working with different real-world datasets and extracting useful insights from them using popular Python libraries such as NumPy, pandas, scikit-learn, and matplotlib. You will then learn the different stages of data mining such as data loading, cleaning, analysis, and visualization. You will also get a full conceptual description of popular data transformation, clustering, and classification techniques. By the end of this book, you will be able to build an efficient data mining pipeline using Python without any hassle. What you will learnExplore the methods for summarizing datasets and visualizing/plotting dataCollect and format data for analytical workAssign data points into groups and visualize clustering patternsLearn how to predict continuous and categorical outputs for dataClean, filter noise from, and reduce the dimensions of dataSerialize a data processing model using scikit-learn’s pipeline featureDeploy the data processing model using Python’s pickle moduleWho this book is for Python developers interested in getting started with data mining will love this book. Budding data scientists and data analysts looking to quickly get to grips with practical data mining with Python will also find this book to be useful. Knowledge of Python programming is all you need to get started.



Data Mining for Business Analytics

Author : Galit Shmueli

Publisher : John Wiley & Sons

Page : 608 pages

File Size : 10,71 MB

Release : 2019-10-14

Category : Mathematics

ISBN : 111954985X

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Book Description

Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process A new section on ethical issues in data mining Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students More than a dozen case studies demonstrating applications for the data mining techniques described End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.” —Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R



Data Science Using Python and R

Author : Chantal D. Larose

Publisher : John Wiley & Sons

Page : 256 pages

File Size : 19,37 MB

Release : 2019-04-09

Category : Computers

ISBN : 1119526817

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Book Description

Learn data science by doing data science! Data Science Using Python and R will get you plugged into the world’s two most widespread open-source platforms for data science: Python and R. Data science is hot. Bloomberg called data scientist “the hottest job in America.” Python and R are the top two open-source data science tools in the world. In Data Science Using Python and R, you will learn step-by-step how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Data Science Using Python and R is written for the general reader with no previous analytics or programming experience. An entire chapter is dedicated to learning the basics of Python and R. Then, each chapter presents step-by-step instructions and walkthroughs for solving data science problems using Python and R. Those with analytics experience will appreciate having a one-stop shop for learning how to do data science using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naïve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining. Further, exciting new topics such as random forests and general linear models are also included. The book emphasizes data-driven error costs to enhance profitability, which avoids the common pitfalls that may cost a company millions of dollars. Data Science Using Python and R provides exercises at the end of every chapter, totaling over 500 exercises in the book. Readers will therefore have plenty of opportunity to test their newfound data science skills and expertise. In the Hands-on Analysis exercises, readers are challenged to solve interesting business problems using real-world data sets.



Python for Data Mining Quick Syntax Reference

Author : Valentina Porcu

Publisher : Apress

Page : 269 pages

File Size : 21,48 MB

Release : 2018-12-19

Category : Computers

ISBN : 1484241134

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Book Description

​Learn how to use Python and its structures, how to install Python, and which tools are best suited for data analyst work. This book provides you with a handy reference and tutorial on topics ranging from basic Python concepts through to data mining, manipulating and importing datasets, and data analysis. Python for Data Mining Quick Syntax Reference covers each concept concisely, with many illustrative examples. You'll be introduced to several data mining packages, with examples of how to use each of them. The first part covers core Python including objects, lists, functions, modules, and error handling. The second part covers Python's most important data mining packages: NumPy and SciPy for mathematical functions and random data generation, pandas for dataframe management and data import, Matplotlib for drawing charts, and scikitlearn for machine learning. What You'll LearnInstall Python and choose a development environment Understand the basic concepts of object-oriented programming Import, open, and edit files Review the differences between Python 2.x and 3.xWho This Book Is For Programmers new to Python's data mining packages or with experience in other languages, who want a quick guide to Pythonic tools and techniques.



Practical Machine Learning for Data Analysis Using Python

Author : Abdulhamit Subasi

Publisher : Academic Press

Page : 534 pages

File Size : 35,62 MB

Release : 2020-06-05

Category : Computers

ISBN : 0128213809

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Book Description

Practical Machine Learning for Data Analysis Using Python is a problem solver’s guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems. Offers a comprehensive overview of the application of machine learning tools in data analysis across a wide range of subject areas Teaches readers how to apply machine learning techniques to biomedical signals, financial data, and healthcare data Explores important classification and regression algorithms as well as other machine learning techniques Explains how to use Python to handle data extraction, manipulation, and exploration techniques, as well as how to visualize data spread across multiple dimensions and extract useful features



Data Mining and Machine Learning

Author : Mohammed J. Zaki

Publisher : Cambridge University Press

Page : 779 pages

File Size : 14,94 MB

Release : 2020-01-30

Category : Business & Economics

ISBN : 1108473989

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Book Description

New to the second edition of this advanced text are several chapters on regression, including neural networks and deep learning.



An Introduction to Statistical Learning

Author : Gareth James

Publisher : Springer Nature

Page : 617 pages

File Size : 14,44 MB

Release : 2023-08-01

Category : Mathematics

ISBN : 3031387473

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Book Description

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.