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

Summary

In this chapter, we explained that we need the assistance of machine learning and the K-means algorithm to find the optimal number of groups and the segments of a dataset because a visual inspection is not accurate or quantitative. We learned how to install and execute R functions to find the optimal number of groups, as well as K-means execution. Once we had the grouping classification, we used pivot tables and charts to find the centroids for each group and the minimum and maximum ranges to get an idea of the distance between the values and the average or centroid. Remember that the best-case scenario is to have compact groups with a small standard deviation. If there were a large distance between values and the average, we'd probably have outliers that could affect the operation in the near future. Also, if a group has large, scattered datasets, we'd probably need to apply the K-means algorithm just for this group to create subsegments. That was the case with the...