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Learn data mining through Excela step-by-step approach for understanding machine learning methods /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Learn data mining through Excelby Hong Zhou.
Reminder of title:	a step-by-step approach for understanding machine learning methods /
Author:	Zhou, Hong.
Published:	Berkeley, CA :Apress :2023.
Description:	xi, 288 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Data mining.
Online resource:	https://doi.org/10.1007/978-1-4842-9771-1
ISBN:	9781484297711$q(electronic bk.)

Learn data mining through Excela step-by-step approach for understanding machine learning methods /
Zhou, Hong.

Learn data mining through Excela step-by-step approach for understanding machine learning methods /[electronic resource] :by Hong Zhou. - Second edition. - Berkeley, CA :Apress :2023. - xi, 288 p. :ill., digital ;24 cm.

Chapter 1: Excel and Data Mining -- Chapter 2: Linear Regression -- Chapter 3: K-Means Clustering -- Chapter 4: Linear Discriminant Analysis -- Chapter 5: Cross Validation and ROC -- Chapter 6: Logistic Regression -- Chapter 7: K-nearest Neighbors -- Chapter 8: Naïve Bayes Classification -- Chapter 9: Decision Trees -- Chapter 10: Association Analysis -- Chapter 11: Artificial Neural Networks -- Chapter 12: Text Mining -- Chapter 13: Hierarchical Clustering and Dendrogram -- Chapter 14 Exploratory Data Analysis (EDA) -- Chapter 15: After Excel.

Use popular data mining techniques in Microsoft Excel to better understand machine learning methods. Most software tools and programming language packages take data input and deliver data mining results directly, presenting no insight on working mechanics and creating a chasm between input and output. This is where Excel can help, and this book will show you exactly how. This updated edition demonstrates how to work with data in a transparent manner using Excel. When you open an Excel file, data is visible immediately and you can work with it directly. You'll see how to examine intermediate results even as you are still conducting your mining task, offering a deeper understanding of how data is manipulated, and results are obtained. These are critical aspects of the model construction process that are often hidden in software tools and programming language packages. Over the course of Learn Data Mining Through Excel, you will learn the data mining advantages the application offers when the data sets are not too large. You'll see how to use Excel's built-in features to create visual representations of your data, enabling you to present your findings in an accessible format. Author Hong Zhou walks you through each step, offering not only an active learning experience, but teaching you how the mining process works and how to find hidden patterns within the data. Upon completing this book, you will have a thorough understanding of how to use an application you very likely already have to mine and analyze data, and how to present results in various formats. You will: Comprehend data mining using a visual step-by-step approach Gain an introduction to the fundamentals of data mining Implement data mining methods in Excel Understand machine learning algorithms Leverage Excel formulas and functions creatively Obtain hands-on experience with data mining and Excel.

ISBN: 9781484297711$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-9771-1doiSubjects--Uniform Titles:

Microsoft Excel (Computer file)
Subjects--Topical Terms:

184440
Data mining.

LC Class. No.: QA76.9.D343 / Z46 2023

Dewey Class. No.: 006.312

Learn data mining through Excela step-by-step approach for understanding machine learning methods /
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505 0 $a Chapter 1: Excel and Data Mining -- Chapter 2: Linear Regression -- Chapter 3: K-Means Clustering -- Chapter 4: Linear Discriminant Analysis -- Chapter 5: Cross Validation and ROC -- Chapter 6: Logistic Regression -- Chapter 7: K-nearest Neighbors -- Chapter 8: Naïve Bayes Classification -- Chapter 9: Decision Trees -- Chapter 10: Association Analysis -- Chapter 11: Artificial Neural Networks -- Chapter 12: Text Mining -- Chapter 13: Hierarchical Clustering and Dendrogram -- Chapter 14 Exploratory Data Analysis (EDA) -- Chapter 15: After Excel.
520 $a Use popular data mining techniques in Microsoft Excel to better understand machine learning methods. Most software tools and programming language packages take data input and deliver data mining results directly, presenting no insight on working mechanics and creating a chasm between input and output. This is where Excel can help, and this book will show you exactly how. This updated edition demonstrates how to work with data in a transparent manner using Excel. When you open an Excel file, data is visible immediately and you can work with it directly. You'll see how to examine intermediate results even as you are still conducting your mining task, offering a deeper understanding of how data is manipulated, and results are obtained. These are critical aspects of the model construction process that are often hidden in software tools and programming language packages. Over the course of Learn Data Mining Through Excel, you will learn the data mining advantages the application offers when the data sets are not too large. You'll see how to use Excel's built-in features to create visual representations of your data, enabling you to present your findings in an accessible format. Author Hong Zhou walks you through each step, offering not only an active learning experience, but teaching you how the mining process works and how to find hidden patterns within the data. Upon completing this book, you will have a thorough understanding of how to use an application you very likely already have to mine and analyze data, and how to present results in various formats. You will: Comprehend data mining using a visual step-by-step approach Gain an introduction to the fundamentals of data mining Implement data mining methods in Excel Understand machine learning algorithms Leverage Excel formulas and functions creatively Obtain hands-on experience with data mining and Excel.
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