Learn Data Mining Through Excel : A Step-by-Step Approach for Understanding Machine Learning Methods / by Hong Zhou.

Format:

Book

Author/Creator:

Zhou, Hong.

Language:

English

Subjects (All):

Microsoft software.

Microsoft .NET Framework.

Data mining.

Microsoft.

Data Mining and Knowledge Discovery.

Local Subjects:

Microsoft.

Data Mining and Knowledge Discovery.

Physical Description:

1 online resource (289 pages)

Edition:

2nd ed. 2023.

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2023.

Summary:

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.

Contents:

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.

Notes:

Includes index.

ISBN:

9781484297711

1484297717

OCLC:

1402228000