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

Determining the groups and average value (centroids) of two and three variables

Now that we know the optimal number of groups for two and three variables (revenue, quantity, and month) by running the Elbow function, we will perform these activities:

  • Getting the groups with the K-means algorithm for two and three variables
  • Visualizing centroids or the average value of each group for two and three variables
  • Charting the product value range of each group for revenue, quantity, and month

For the revenue and quantity variables, we can visualize the minimum and maximum values of quantity for the best revenue group.

Also using three variables (revenue, quantity, and month of sale), we can explore which months of the year demand a higher quantity of products and what are the revenue ranges to see whether it is worth moving this large logistic operation to get the revenue.

Getting the groups with the K-means algorithm for two and three variables

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