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

Finding an optimal number of groups for one variable

The first task to solve grouping statistics is to find out the optimal number of groups for our data. Remember these facts by looking at Figure 5.1. Minimize the distance of each group point to its centroid or group average.

The optimal distance is a small standard deviation result of the group data. Data that is at a large distance from the group centroid is an outlier. This means that we need to further research these points because they could represent risky behavior.

Knowing these facts, look at Figure 5.1 and see how difficult it is to decide, at a glance, how many groups have the optimal sales per product and the number of absent hours due to sickness for a human resources case study:

Figure 5.1 – A: Revenue per country, B: Absent hours per disease and month

To get the optimal number of groups, we need the K-means elbow algorithm chart. Choose the number where the curve starts to get flat...