Book Image

Data Forecasting and Segmentation Using Microsoft Excel

By : Fernando Roque
Book Image

Data Forecasting and Segmentation Using Microsoft Excel

By: Fernando Roque

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.
Table of Contents (19 chapters)
1
Part 1 – An Introduction to Machine Learning Functions
5
Part 2 – Grouping Data to Find Segments and Outliers
10
Part 3 – Simple and Multiple Linear Regression Analysis
14
Part 4 – Predicting Values with Time Series

Chapter 7: Analyzing Outliers for Data Anomalies

In this chapter, we are going to use the K-means grouping function to find the outliers of three of the most used datasets in Kaggle: credit card fraud detection, suspicious logins, and insurance money amount complaints.

2D and 3D charts help us to understand the possible outliers that could lead to fraud in credit card transactions, possible security breaches in login attempts, and the special cases that demand more money from insurance companies.

The methodology of this chapter is to visualize the outliers in charting 2D and 3D variables to get familiar with the data and find possible out-of-the-ordinary behavior and the possible number of groups. Then, we'll use pivot chart business intelligence to classify the ranges of the groups and identify the groups and variables that lead to outliers.

With these practical datasets, we will get experience in applying the K-means function add-in of Excel to other real data. This...