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Hands-On Machine Learning with Microsoft Excel 2019

You're reading from  Hands-On Machine Learning with Microsoft Excel 2019

Product type Book
Published in Apr 2019
Publisher Packt
ISBN-13 9781789345377
Pages 254 pages
Edition 1st Edition
Languages
Author (1):
Julio Cesar Rodriguez Martino Julio Cesar Rodriguez Martino
Profile icon Julio Cesar Rodriguez Martino
Toc

Table of Contents (17) Chapters close

Preface 1. Section 1: Machine Learning Basics
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

Studying the Spearman's correlation

To calculate the Spearman's coefficient, we need to first rank the values of each variable, that is, the order of the values when we sort them from highest to lowest. Once we have the new table, we will calculate Pearson's ρ on it.

In a new sheet, we define the following formula in a cell:

=RANK.AVG(Data!A2;auto_mpg[mpg])

Here, we are asking Excel to write in that cell the ranking corresponding to the first cell of the mpg column in our data table, taking into account the full range of the column. We copy the formula to the cells on the right until we complete the number of columns of the data table (8 columns). It doesn't matter if you copy the formula to an extra cell – you will just get an error message since you are out of the data table range. In a similar way, we can copy the formulas to the remaining...

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