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

Running the K-means function to get the centroids or group average

Now, we'll pass a parameter for the number of groups we want to process with K-means. This is a parameter in cell C1:1 for the function. Also, we include the range of input values and the range where the function will insert the group assignment values.

To execute the K-means function, take the following steps (and also see Figure 5.6):

  1. Insert the number of groups to apply segmentation.
  2. Define the Excel sheet data input range.
  3. Specify the Excel sheet range to store the group segmentation results.

With the results, we will do a pivot analysis to understand the group assignment to every value of the data input.

Figure 5.6 – Parameters to execute K-means

Figure 5.6 shows how we specify the parameters of the K-means function. We can explain this process step by step as follows:

  1. Insert the number of groups to apply segmentation.

The first parameter...