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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

Section 1: Machine Learning Basics

Section 1: Machine Learning Basics

Implementing Machine Learning Algorithms

Implementing Machine Learning Algorithms

Technical requirements

Understanding learning and models

Focusing on model features

Studying machine learning models in practice

Comparing underfitting and overfitting

Evaluating models

Further reading

Hands-On Examples of Machine Learning Models

Hands-On Examples of Machine Learning Models

Technical requirements

Understanding supervised learning with multiple linear regression

Understanding supervised learning with decision trees

Understanding unsupervised learning with clustering

Further reading

Section 2: Data Collection and Preparation

Section 2: Data Collection and Preparation

Importing Data into Excel from Different Data Sources

Importing Data into Excel from Different Data Sources

Technical requirements

Importing data from a text file

Importing data from another Excel workbook

Importing data from a web page

Importing data from Facebook

Importing data from a JSON file

Importing data from a database

Further reading

Data Cleansing and Preliminary Data Analysis

Data Cleansing and Preliminary Data Analysis

Technical requirements

Visualizing data for preliminary analysis

Understanding unbalanced datasets

Further reading

Correlations and the Importance of Variables

Correlations and the Importance of Variables

Technical requirements

Building a scatter diagram

Calculating the covariance

Calculating the Pearson's coefficient of correlation

Studying the Spearman's correlation

Understanding least squares

Focusing on feature selection

Further reading

Section 3: Analytics and Machine Learning Models

Section 3: Analytics and Machine Learning Models

Data Mining Models in Excel Hands-On Examples

Data Mining Models in Excel Hands-On Examples

Technical requirements

Learning by example – Market Basket Analysis

Learning by example – Customer Cohort Analysis

Further reading

Implementing Time Series

Implementing Time Series

Technical requirements

Modeling and visualizing time series

Forecasting time series automatically in Excel

Studying the stationarity of a time series

Further reading

Section 4: Data Visualization and Advanced Machine Learning

Section 4: Data Visualization and Advanced Machine Learning

Visualizing Data in Diagrams, Histograms, and Maps

Visualizing Data in Diagrams, Histograms, and Maps

Technical requirements

Showing basic comparisons and relationships between variables

Building data distributions using histograms

Representing geographical distribution of data in maps

Showing data that changes over time

Further reading

Artificial Neural Networks

Artificial Neural Networks

Technical requirements

Introducing the perceptron – the simplest type of neural network

Building a deep network

Understanding the backpropagation algorithm

Further reading

Azure and Excel - Machine Learning in the Cloud

Azure and Excel - Machine Learning in the Cloud

Technical requirements

Introducing the Azure Cloud

Using AMLS for free – a step-by-step guide

Loading your data into AMLS

Creating and running an experiment in AMLS

Further reading

The Future of Machine Learning

The Future of Machine Learning

Automatic data analysis flows

Automated machine learning

Further reading

Assessment

Chapter 1, Implementing Machine Learning Algorithms

Chapter 2, Hands-On Examples of Machine Learning Models

Chapter 3, Importing Data into Excel from Different Data Sources

Chapter 4, Data Cleansing and Preliminary Data Analysis

Chapter 5, Correlations and the Importance of Variables

Chapter 6, Data Mining Models in Excel Hands-On Examples

Chapter 7, Implementing Time Series

Chapter 8, Visualizing Data in Diagrams, Histograms, and Maps

Chapter 9, Artificial Neural Networks

Chapter 10, Azure and Excel - Machine Learning in the Cloud

Chapter 11, The Future of Machine Learning

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To get the most out of this book

You will need working knowledge of Excel, including how to make cell calculations, input basic functions, and make diagrams. For Chapter 10, Azure and Excel - Machine Learning in the Cloud you will need a Microsoft account.

Download the example code files

You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packt.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

Log in or register at www.packt.com.
Select the SUPPORT tab.
Click on Code Downloads & Errata.
Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

WinRAR/7-Zip for Windows
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The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Hands-On-Machine-Learning-with-Microsoft-Excel-2019. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: http://www.packtpub.com/sites/default/files/downloads/9781789345377_ColorImages.pdf.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Navigate to the file's location and open the homes.csv file."

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Select the full range of cells containing the table, click on Insert menu, and select Charts."

Warnings or important notes appear like this.

Tips and tricks appear like this.