#### 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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# Pivot analysis of the outliers

We can apply the business intelligence pivot tables to explore the ranges of the groups for every variable of the dataset. Using this method, we can visualize the groups that appear to be outliers.

## Kaggle credit card fraud dataset

With the information of the group assignment with K-means clustering, we can explore the outliers for each dataset. From the amount chart of credit card transactions in Figure 7.9, we see that groups three and four have compact and similar values with a combined range between 355 and 1402:

Figure 7.9 – Credit card amount field groups

From Figure 7.9, we could conclude that the possible outliers are as follows:

• Group 1 (ranges between 0 and 86)
• Group 5 (ranges between 89 and 322)
• Group 4 (has just one record with a big value of 3828, which indicates an anomaly)

Combining the analysis with the V1 field groups, in Figure 7.10, we can examine whether we can confirm the...