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Hands-On Machine Learning with Microsoft Excel 2019
Hands-On Machine Learning with Microsoft Excel 2019

Hands-On Machine Learning with Microsoft Excel 2019: Build complete data analysis flows, from data collection to visualization

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Hands-On Machine Learning with Microsoft Excel 2019

Implementing Machine Learning Algorithms

Learning has been a matter of study for many years. How human beings acquire new knowledge, from basic survival skills to advanced abstract subjects, is difficult to understand and reproduce in the computer world. Machines learn by comparing examples and by finding similarities in them.

The easiest way for a machine (and also for a human being) to learn is to simplify the problem that needs to be solved. A simplified version of reality, called a model, is useful for this task. Some of the relevant issues to be studied are the minimum number of samples, underfitting and overfitting, relevant features, and how well a model can learn. Different types of target variables require different algorithms.

In this chapter, the following topics will be covered:

  • Understanding learning and models
  • Focusing on model features
  • Studying machine learning...

Technical requirements

There are no technical requirements for this chapter, since it is introductory. The data shown in the sections should be input into an Excel spreadsheet in order to be able to follow the examples.

Understanding learning and models

The way that humans learn has been studied for many decades now. There are a handful of psychological theories that try to explain how we acquire knowledge, use it, and generalize it in order to apply what we know to completely new scenarios. Taking one step back, we could ask ourselves: what does it mean to learn? We could say that, once we learn something, we are able to repeat it in a more or less detailed way. In reality, learning implies much more than just copying a behavior or memorizing a piece of poetry. In fact, we understand what we learn and are able to generalize that knowledge, which helps us to react correctly to new people, places, and situations.

The need to create a machine that somehow mimics our human behavior and intelligence has been desired for a very long time. Hundreds of years ago, kings were amazed by chess-playing machines...

Focusing on model features

As a simplified representation of reality, a model also includes a set of variables that contain the relevant information that describes the different parts of the problem we are representing. These variables can be something as concrete as 1 kg of ice cream, as we saw in our previous example, or as abstract as a numerical value that represents how similar the meaning is of two words in a text document.

In the particular case of a machine learning model, these variables are called features. Choosing significant features that provide relevant information about the phenomenon that we try to explain or predict is of paramount importance. If we consider unsupervised learning, then the relevant features are those that better represent the clustering or association of information in the dataset. For supervised learning, the most important features are those...

Studying machine learning models in practice

We have already seen a very simple example and used it to explain some basic concepts. In the next chapter, we are going to explore more complex models. We restricted ourselves to a very small dataset, just for clarity and to start our journey towards mastering machine learning with an easy task. There are some general considerations that we need to be aware of when working with machine learning models to solve real problems:

  • The amount of data is usually very large. In fact, a larger dataset helps to get a more accurate model and a more reliable prediction. Extremely large datasets, usually called big data, can present storage and manipulation challenges.
  • Data is never clean and ready to use, so data cleansing is extremely important and takes a lot of time.
  • The number of features required to correctly represent a real-life problem...

Comparing underfitting and overfitting

In the preceding list, step 4 implies an iterative process where we try models, parameters, and features until we get the best result that we can. Let's now think about a classification problem, where we want to separate squares from circles, as shown in the following diagram. At the beginning of the process, we will probably be in a situation that is similar to the first chart (on the left-hand side). The model fails to efficiently separate the two shapes and both sides are a mixture of both squares and circles. This is called underfitting and refers to a model that fails to represent the characteristics of the dataset:

As we continue tuning parameters and adjusting the model to the training dataset, we might find ourselves in a situation that is similar to the third chart (on the right-hand side). The model accurately splits the dataset...

Evaluating models

Whenever we obtain a result, it is is only as accurate as the model that represents the real problem. It is, therefore, extremely important to understand which methods can be used to evaluate the performance of our models.

When dealing with classification models we can use the following methods.

Analyzing classification accuracy

This is the ratio of the number of correct predictions (CP) to the total number of samples:

Here, CP is the number of accurate or correct predictions, and TP is the total count of all the predictions that have been made.

Building the confusion matrix

...

Summary

In this chapter, we briefly discussed the learning process for machines, which, to some extent, mimics that of human beings. We described how a model, which is a simplified representation of the problem that we want to solve, can be used to apply machine learning to find a solution.

Using a linear regression model, we built a simple supervised predictive model and explained how to use it. We then discussed the difference between regression and classification, and showed the properties of the input variables and features.

Underfitting and overfitting are two of the main concerns when training a machine learning model. We explained what they are and suggested methods to avoid them.

Finally, different types of target variables require different algorithms and evaluation methods to test the quality of the model – we discussed this in detail in the final sections.

In...

Questions

  1. What is the main difference between classical computer programming and machine learning?
  2. How are models classified, considering the type of target variable?
  3. What are the different types of models, depending on how they learn?
  4. What are the main steps when creating and using a machine learning model?
  5. The output of the regression performed in Excel contains information about the residuals. What are they and how are they related to the MAE and MSE?
  6. Explain underfitting and overfitting.
  7. How can categorical features be used to feed machine learning models?

Further reading

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Key benefits

  • Use Microsoft's product Excel to build advanced forecasting models using varied examples
  • Cover range of machine learning tasks such as data mining, data analytics, smart visualization, and more
  • Derive data-driven techniques using Excel plugins and APIs without much code required

Description

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.

Who is this book for?

This book is for data analysis, machine learning enthusiasts, project managers, and someone who doesn't want to code much for performing core tasks of machine learning. Each example will help you perform end-to-end smart analytics. Working knowledge of Excel is required.

What you will learn

  • Use Excel to preview and cleanse datasets
  • Understand correlations between variables and optimize the input to machine learning models
  • Use and evaluate different machine learning models from Excel
  • Understand the use of different visualizations
  • Learn the basic concepts and calculations to understand how artificial neural networks work
  • Learn how to connect Excel to the Microsoft Azure cloud
  • Get beyond proof of concepts and build fully functional data analysis flows
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Table of Contents

16 Chapters
Section 1: Machine Learning Basics Chevron down icon Chevron up icon
Implementing Machine Learning Algorithms Chevron down icon Chevron up icon
Hands-On Examples of Machine Learning Models Chevron down icon Chevron up icon
Section 2: Data Collection and Preparation Chevron down icon Chevron up icon
Importing Data into Excel from Different Data Sources Chevron down icon Chevron up icon
Data Cleansing and Preliminary Data Analysis Chevron down icon Chevron up icon
Correlations and the Importance of Variables Chevron down icon Chevron up icon
Section 3: Analytics and Machine Learning Models Chevron down icon Chevron up icon
Data Mining Models in Excel Hands-On Examples Chevron down icon Chevron up icon
Implementing Time Series Chevron down icon Chevron up icon
Section 4: Data Visualization and Advanced Machine Learning Chevron down icon Chevron up icon
Visualizing Data in Diagrams, Histograms, and Maps Chevron down icon Chevron up icon
Artificial Neural Networks Chevron down icon Chevron up icon
Azure and Excel - Machine Learning in the Cloud Chevron down icon Chevron up icon
The Future of Machine Learning Chevron down icon Chevron up icon
Assessment Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Full star icon 5
(3 Ratings)
5 star 100%
4 star 0%
3 star 0%
2 star 0%
1 star 0%
Omar Ernesto Cabrera Rosero Jul 18, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book is very well explained literature, the practical examples are very well detailed. It is very explained the import of data from different sources, data cleansing, exploratory data analysis and examples of data mining models, it is very interesting the integration chapter of Azure ML and Excel that is very useful when creating models and results are consumed by different users for making decisions.
Amazon Verified review Amazon
K Johnson Dec 27, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
One of the best books on the subject.
Amazon Verified review Amazon
Ruth Munoz Oct 07, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
It's an amazing book to start learning ML without programming tools, just excel. It's easy to ready and it provides you a complete guide to start from scratch with the subject. It has very good examples and real-life problems to practice
Amazon Verified review Amazon
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