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Hands-On Machine Learning with Microsoft Excel 2019

You're reading from   Hands-On Machine Learning with Microsoft Excel 2019 Build complete data analysis flows, from data collection to visualization

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781789345377
Length 254 pages
Edition 1st Edition
Tools
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Author (1):
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Julio Cesar Rodriguez Martino Julio Cesar Rodriguez Martino
Author Profile Icon Julio Cesar Rodriguez Martino
Julio Cesar Rodriguez Martino
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Table of Contents (17) Chapters Close

Preface 1. Section 1: Machine Learning Basics FREE CHAPTER
2. Implementing Machine Learning Algorithms 3. Hands-On Examples of Machine Learning Models 4. Section 2: Data Collection and Preparation
5. Importing Data into Excel from Different Data Sources 6. Data Cleansing and Preliminary Data Analysis 7. Correlations and the Importance of Variables 8. Section 3: Analytics and Machine Learning Models
9. Data Mining Models in Excel Hands-On Examples 10. Implementing Time Series 11. Section 4: Data Visualization and Advanced Machine Learning
12. Visualizing Data in Diagrams, Histograms, and Maps 13. Artificial Neural Networks 14. Azure and Excel - Machine Learning in the Cloud 15. The Future of Machine Learning 16. Assessment

Chapter 5, Correlations and the Importance of Variables

  1. You can, for example, build a diagram with the categorical values on the x axis and the numerical values on the y axis; any correlation would be clear from this diagram.
  2. It should be easy for the reader to build diagrams and understand the relationship between variables.
  3. No. It means that when a variable increases, the other variable decreases.
  4. This formatting was used in Chapter 6, Data Mining Models in Excel Hands-On Examples.
  5. We calculated the Squared Error (SSE) as ([@mpg]-[@prediction])^2. The other sum we need is SST = ([@mpg]-average([@prediction]))^2. Then, we calculate R2 = 1-SSE/SST.
  6. You can try using an exponential function (EXP()) or another function with a similar shape. The R2 value will probably still be far from 1, since the dispersion in the data is very high.
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