You're reading from Data Forecasting and Segmentation Using Microsoft Excel Perform data grouping, linear predictions, and time series machine learning statistics without using code

Product type Paperback

Published in May 2022

Publisher Packt

ISBN-13 9781803247731

Length 324 pages

Edition 1st Edition

Languages

C++

Tools

Excel

Concepts

Data Analysis

Author (1):

Fernando Roque

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Table of Contents (19) Chapters

Preface

1. Part 1 – An Introduction to Machine Learning Functions

2. Chapter 1: Understanding Data Segmentation FREE CHAPTER

3. Chapter 2: Applying Linear Regression

4. Chapter 3: What is Time Series?

5. Part 2 – Grouping Data to Find Segments and Outliers

6. Chapter 4: Introduction to Data Grouping

7. Chapter 5: Finding the Optimal Number of Single Variable Groups

8. Chapter 6: Finding the Optimal Number of Multi-Variable Groups

9. Chapter 7: Analyzing Outliers for Data Anomalies

10. Part 3 – Simple and Multiple Linear Regression Analysis

11. Chapter 8: Finding the Relationship between Variables

12. Chapter 9: Building, Training, and Validating a Linear Model

13. Chapter 10: Building, Training, and Validating a Multiple Regression Model

14. Part 4 – Predicting Values with Time Series

15. Chapter 11: Testing Data for Time Series Compliance

16. Chapter 12: Working with Time Series Using the Centered Moving Average and a Trending Component

17. Chapter 13: Training, Validating, and Running the Model

18. Other Books You May Enjoy

Chapter 6: Finding the Optimal Number of Multi-Variable Groups

In this chapter, we will expand the use of the Elbow and K-means functions for a multivariable analysis from two to four variables. We will use the Elbow function to find the optimal number of groups and then pass it as a parameter to the K-means function. The objective of the example is to find the highest revenue group that delivers smaller quantities of items to avoid logistics costs. The K-means function is useful for this multi-dimensional research in that it combines all these variables and returns the segments evaluating the variable's influence. We will explore the data scattered by exploring the distance between the data points of each group and its centroid.

The topics we will cover in this chapter are as follows:

Calculating the optimal number of groups with two and three variables
Determining the groups and average value (centroids) of two and three variables
Using the Elbow and K-means...

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You're reading from Data Forecasting and Segmentation Using Microsoft Excel Perform data grouping, linear predictions, and time series machine learning statistics without using code

Table of Contents (19) Chapters

Chapter 6: Finding the Optimal Number of Multi-Variable Groups

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You're reading from Data Forecasting and Segmentation Using Microsoft Excel Perform data grouping, linear predictions, and time series machine learning statistics without using code

Table of Contents (19) Chapters

Chapter 6: Finding the Optimal Number of Multi-Variable Groups

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