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Learn Data Mining Through Excel: A Step-by-step Approach for Understanding Machine Learning Methods

โœ Scribed by Hong Zhou


Publisher
Apress
Year
2020
Tongue
English
Leaves
223
Edition
1
Category
Library

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No coin nor oath required. For personal study only.

โœฆ Synopsis


Use popular data mining techniques in Microsoft Excel to better understand machine learning methods.

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.

Excel allows you to work with data in a transparent manner. When you open an Excel file, data is visible immediately and you can work with it directly. Intermediate results can be examined while you are 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 hidden in software tools and programming language packages.

This book teaches you data mining through Excel. You will learn how Excel has an advantage in data mining when the data sets are not too large. It can give you a visual representation of data mining, building confidence in your results. You will go through every step manually, which offers not only an active learning experience, but teaches you how the mining process works and how to find the internal hidden patterns inside the data.


What You Will Learn

  • Comprehend data mining using a visual step-by-step approach
  • Build on a theoretical introduction of a data mining method, followed by an Excel implementation
  • Unveil the mystery behind machine learning algorithms, making a complex topic accessible to everyone
  • Become skilled in creative uses of Excel formulas and functions
  • Obtain hands-on experience with data mining and Excel


Who This Book Is For

Anyone who is interested in learning data mining or machine learning, especially data science visual learners and people skilled in Excel, who would like to explore data science topics and/or expand their Excel skills. A basic or beginner level understanding of Excel is recommended. 


โœฆ Table of Contents


Table of Contents
About the Author
About the Technical Reviewer
Acknowledgments
Introduction
Chapter 1: Excel andย Data Mining
Why Excel?
Prepare Some Excel Skills
Formula
Autofill or Copy
Absolute Reference
Paste Special andย Paste Values
IF Function Series
Review Points
Chapter 2: Linear Regression
General Understanding
Learn Linear Regression Through Excel
Learn Multiple Linear Regression Through Excel
Review Points
Chapter 3: K-Means Clustering
General Understanding
Learn K-Means Clustering Through Excel
Review Points
Chapter 4: Linear Discriminant Analysis
General Understanding
Solver
Learn LDA Through Excel
Review Points
Chapter 5: Cross-Validation andย ROC
General Understanding ofย Cross-Validation
Learn Cross-Validation Through Excel
General Understanding ofย ROC Analysis
Learn ROC Analysis Through Excel
Review Points
Chapter 6: Logistic Regression
General Understanding
Learn Logistic Regression Through Excel
Review Points
Chapter 7: K-Nearest Neighbors
General Understanding
Learn K-NN Through Excel
Experiment 1
Experiment 2
Experiment 3
Experiment 4
Review Points
Chapter 8: Naรฏve Bayes Classification
General Understanding
Learn Naรฏve Bayes Through Excel
Exercise 1
Exercise 2
Review Points
Chapter 9: Decision Trees
General Understanding
Learn Decision Trees Through Excel
Learn Decision Trees Through Excel
A Better Approach
Apply theย Model
Review Points
Chapter 10: Association Analysis
General Understanding
Learn Association Analysis Through Excel
Review Points
Chapter 11: Artificial Neural Network
General Understanding
Learn Neural Network Through Excel
Experiment 1
Experiment 2
Review Points
Chapter 12: Text Mining
General Understanding
Learn Text Mining Through Excel
Review Points
Chapter 13: After Excel
Index


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