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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

Representing geographical distribution of data in maps

Localizing information in a map is extremely useful to understand data in the spatial dimension, which is often difficult by other means. Excel offers different options and we are going to show a couple of them. We will start by using data containing geographical coordinates, that is, latitude and longitude. The widespread use of the GPS system nowadays makes it easy to obtain this information. In any case, if this precise information is not available, we will see that the built-in information in Excel will still make it simple to draw maps and show us useful information.

In our first example, we will use data from the Massachusetts Institute of Technology Election Data and Science Lab about the 1976 US presidential election. The 1976USpresident.xlsx file contains, among other information, the list of US states and the vote...

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