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

Summary

In this chapter, we explored different methods of dealing with missing data and learned how to group or summarize it. We have shown you how important it is to visualize the data after cleaning, in order to be able to understand and interpret the results, from basic to more advanced model predictions. This is the beginning of any feature engineering, since we transform and/or discard features based on their values. Too many missing values will imply that we cannot use that variable (or feature), or a high correlation will imply that we can discard one of the correlated variables. We will dive deeper into correlations in the next chapter, showing you how to measure them quantitatively, using different methods.

Preliminary data visualization is extremely important to gain an understanding of data properties and to interpret the results we obtain, even after applying a machine...