Book Image

Hands-On Machine Learning with Microsoft Excel 2019

By : Julio Cesar Rodriguez Martino
Book Image

Hands-On Machine Learning with Microsoft Excel 2019

By: Julio Cesar Rodriguez Martino

Overview of this book

We have made huge progress in teaching computers to perform difficult tasks, especially those that are repetitive and time-consuming for humans. Excel users, of all levels, can feel left behind by this innovation wave. The truth is that a large amount of the work needed to develop and use a machine learning model can be done in Excel. The book starts by giving a general introduction to machine learning, making every concept clear and understandable. Then, it shows every step of a machine learning project, from data collection, reading from different data sources, developing models, and visualizing the results using Excel features and offerings. In every chapter, there are several examples and hands-on exercises that will show the reader how to combine Excel functions, add-ins, and connections to databases and to cloud services to reach the desired goal: building a full data analysis flow. Different machine learning models are shown, tailored to the type of data to be analyzed. At the end of the book, the reader is presented with some advanced use cases using Automated Machine Learning, and artificial neural network, which simplifies the analysis task and represents the future of machine learning.
Table of Contents (17 chapters)
Free Chapter
1
Section 1: Machine Learning Basics
4
Section 2: Data Collection and Preparation
8
Section 3: Analytics and Machine Learning Models
11
Section 4: Data Visualization and Advanced Machine Learning

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...