#### 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
Other Books You May Enjoy

# Representing the data in a 3D chart

The first example is to apply our knowledge of grouping to find the outliers as possible candidates for research on fraud. For example, we could identify outliers with small amounts of spending and very early transaction hours across 6 consecutive days. This behavior will probably not correspond to the average amount of and typical working hours of transactions.

## Kaggle credit card fraud dataset

The credit card fraud data has several columns:

• The number of seconds since the first transaction was recorded in the dataset.
• The amount expended by the cardholder.
• The `V1` to `V12` columns represent encrypted data to protect the original information. These are numerical fields, and the K-means algorithm can classify these values into groups.

The only true values of the data are `seconds` and `amount`. The `V1` to `V2` fields have data alteration with encryption techniques for privacy measures.

We are going to perform statistical grouping...