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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

Table of Contents (17) Chapters

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

Understanding supervised learning with multiple linear regression

In the previous chapter, we followed an example of linear regression using two variables. It is interesting to see how we can apply regression to more than two variables (called multiple linear regression) and extract useful information from the results.

Suppose that you are asked to test whether there exists a hidden policy of gender discrimination in a company. You could be working for a law firm that is leading a trial against this company, and they need data-based evidence to back up their claim.

You would start by taking a sample of the company's payroll, including several variables that describe each employee and the last salary increase amount. The following screenshot shows a set of values after they've been entered in an Excel worksheet:

There are four numerical features in the dataset:

  • ID:...
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