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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

Questions

  1. What method would be better to find a correlation between a numerical and a categorical variable?
  2. Build some other plot graphs between a pair of variables and study the correlations and the logic behind them.
  3. Does a negative Pearson coefficient value imply that one of the variables has negative values?
  4. The table of the Pearson's coefficient can be colored or have bars added to it in order to better compare the different values. Explore these options in Quick Analysis | Formatting.
  5. The quality of the least square regression is usually measured by the value of R2. Calculate this value for the function that was adjusted in the mpg column versus the weight data value (hint: you only need to calculate one more sum of values – refer to the literature for more information).
  6. The value that was calculated in the previous question should be close to 0.7, which is...
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