#### Overview of this book

Data Forecasting and Segmentation Using Microsoft Excel guides you through basic statistics to test whether your data can be used to perform regression predictions and time series forecasts. The exercises covered in this book use real-life data from Kaggle, such as demand for seasonal air tickets and credit card fraud detection. You’ll learn how to apply the grouping K-means algorithm, which helps you find segments of your data that are impossible to see with other analyses, such as business intelligence (BI) and pivot analysis. By analyzing groups returned by K-means, you’ll be able to detect outliers that could indicate possible fraud or a bad function in network packets. By the end of this Microsoft Excel book, you’ll be able to use the classification algorithm to group data with different variables. You’ll also be able to train linear and time series models to perform predictions and forecasts based on past data.
Preface
Part 1 – An Introduction to Machine Learning Functions
Free Chapter
Chapter 1: Understanding Data Segmentation
Chapter 2: Applying Linear Regression
Chapter 3: What is Time Series?
Part 2 – Grouping Data to Find Segments and Outliers
Chapter 4: Introduction to Data Grouping
Chapter 5: Finding the Optimal Number of Single Variable Groups
Chapter 6: Finding the Optimal Number of Multi-Variable Groups
Chapter 7: Analyzing Outliers for Data Anomalies
Part 3 – Simple and Multiple Linear Regression Analysis
Chapter 8: Finding the Relationship between Variables
Chapter 9: Building, Training, and Validating a Linear Model
Chapter 10: Building, Training, and Validating a Multiple Regression Model
Part 4 – Predicting Values with Time Series
Chapter 11: Testing Data for Time Series Compliance
Chapter 12: Working with Time Series Using the Centered Moving Average and a Trending Component
Chapter 13: Training, Validating, and Running the Model
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# Using the Elbow and K-means functions with four variables

Now, we are going to add a fourth variable, the shipping cost, to the analysis. With this information, we will explore the influence of the logistics demanding infrastructure on the profits of the company. Remember that we pursue to get more revenue without investing more in fixed costs such as delivery operations. We will apply our knowledge of the Elbow and K-means functions to get the optimal number of groups, and then get the segmentation with K-means. See Figure 6.24 where the elbow function returns the number of groups. There is no chance to plot a chart of four variables, so we will have to trust in our expertise and judgment to interpret the groups that K-means will return.

Figure 6.24 – Elbow chart for four variables

In Figure 6.24, we can see that the optimal number of groups for the four variables (revenue, month, quantity, and shipping costs) is eight because this is when the curve...