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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

Importing data from a text file

The more commonly used data text file is comma-separated values (CSV). As the name suggests, values are written to the file in rows and, for each row, a comma separates the values belonging to each column. Open a new workbook and follow these steps:

  1. Click on Data.
  2. Navigate to Get Data | From File | From Text/CSV:
  1. Navigate to the file's location and open the homes.csv file.
  1. A window will pop up, showing you a preview of the file's contents, as shown in the following screenshot:

We can see that Excel correctly identifies the different columns by using the right delimiter (comma). It also tries to detect the data types automatically. There is a small problem, though. This file is not pure CSV, and has an extra line at the beginning, showing us where it was downloaded from originally. This is good for giving credit to the original...

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