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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

Loading your data into AMLS

There is no machine learning project without data, so the first step in our analysis is to load the input file (titanic_small.csv) into AMLS. This is a simplified version of the Titanic dataset, which contains three features and one target variable:

  • Features:
    • pclass: The class in which the passenger traveled (values 1, 2, or 3 corresponding to 1st, 2nd, and 3rd class)
    • sex: Passenger's gender (female or male)
    • Age group: Infant, child, teenager, adult, elderly, or unknown
  • Target variable:
    • Survived: 1 if the passenger survived the shipwreck, 0 if they didn't.

To load the file, follow these steps:

  1. From the home page, click on DATASETS. You will see an empty list of datasets:
  1. Click on +NEW to get a link to upload a local data file:
  1. Click on FROM LOCAL FILE and you will see the following dialog box:
  1. Click on Choose File and navigate...
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