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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K-means data grouping

Before running the K-means function, we need to calculate the parameter of the optimal number of groups. In the last section, we saw that the first approach is to visualize the 2D and 3D charts. However, the best way to get the number of groups is by calculating the elbow function and choosing the number of groups when the curve of the elbow starts to flatten.

Running the elbow algorithm

Now that we have an idea of the number of groups for the credit card fraud dataset, we are ready to use the elbow K-means algorithm to get a statistical value of the optimal number of groups.

Kaggle credit card fraud dataset

Before we run the K-means function, we have to calculate the optimal number of groups for the `V1`, `Time` (seconds), and `Amount` fields of the credit card transactions.

We run the elbow algorithm, changing the range of data in line `3` of the `kmeanselbow.r` add-in, as you can see in Figure 7.4. Use the `credicard01.xlsx` file included in this chapter...