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Data Forecasting and Segmentation Using Microsoft Excel

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

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Product type Paperback
Published in May 2022
Publisher Packt
ISBN-13 9781803247731
Length 324 pages
Edition 1st Edition
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Author (1):
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Fernando Roque Fernando Roque
Author Profile Icon Fernando Roque
Fernando Roque
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Table of Contents (19) Chapters Close

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

Summary

In this chapter, we applied the Elbow and K-means functions to find the optimal number of groups and segments for two and three variables. Once we obtained the results, we did a business intelligence analysis using pivot tables and charts. In the product sales example developed in this chapter, we used segmentation to find the groups that generated higher revenues and what quantity of items were delivered. Grouping helps to achieve the target of selling smaller quantities of products to avoid logistics costs. The four variables segmentation analysis showed that the group with the highest revenue was not the group delivering more items, nor was it the most expensive in terms of shipping costs. Multivariable K-means segmentation helps to do this complex analysis, which would be impossible with just business intelligence and pivot tables and charts. In this chapter, you learned to combine different variables with different units, such as quantity and profit, to research which...

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