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

Understanding least squares

In some instances, we might want to prove that there is a functional relationship between two variables and, hence, just use one of them in our model – since the other can be easily approximated by an expression. In this case, it is useful to rely on the least squares method. Given a set of points (xi,yi) and a function such as y'i = f(xi), this method minimizes the square of the differences between y'i and yi. The general expression for the minimization that we are calculating is as follows:

We will use two columns from our data table, namely weight and mpg:

  1. Create a new table in a new sheet.
  2. Copy the values of the weight and mpg columns.
  1. Order the rows by the value of weight; the resulting table is as follows:
  1. Insert a line chart to see what the functional relationship looks like, as follows:

Let's say that we assume...