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

Visualizing data for preliminary analysis

After cleaning the dataset, it is always recommended to visualize it. This helps us gain an understanding of the different variables, how their values are distributed, and the correlations that exist between them (we will explore correlations in more detail in the next chapter). We can determine which variables are important to our analyses, which ones give us more information, and which ones can be discarded for being redundant.

We will start by looking a several bar plots, where we will either count the number of occurrences of each value (using a histogram), or we will show the percentage of each value with respect to the total (using a bar plot). To achieve this, perform the following steps:

  1. Right-click on any cell within the table to access the Quick Analysis option:
  1. In the pop-up window, we can choose the chart type. Select Clustered...