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.
Preface
Part 1 – An Introduction to Machine Learning Functions
Free Chapter
Chapter 1: Understanding Data Segmentation
Chapter 2: Applying Linear Regression
Chapter 3: What is Time Series?
Part 2 – Grouping Data to Find Segments and Outliers
Chapter 4: Introduction to Data Grouping
Chapter 5: Finding the Optimal Number of Single Variable Groups
Chapter 6: Finding the Optimal Number of Multi-Variable Groups
Chapter 7: Analyzing Outliers for Data Anomalies
Part 3 – Simple and Multiple Linear Regression Analysis
Chapter 8: Finding the Relationship between Variables
Chapter 9: Building, Training, and Validating a Linear Model
Chapter 10: Building, Training, and Validating a Multiple Regression Model
Part 4 – Predicting Values with Time Series
Chapter 11: Testing Data for Time Series Compliance
Chapter 12: Working with Time Series Using the Centered Moving Average and a Trending Component
Chapter 13: Training, Validating, and Running the Model
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Summary

In this chapter, we learned why it's important to find the optimal number of groups before we conduct K-means clustering. Once we have the groups, we analyze whether they are compliant with the best-case scenario for segments having a small standard deviation. Research outliers to find out whether their behavior could lead to further investigation, such as fraud detection.

We need a machine learning function such as K-means clustering to segment data because classifying by simple inspection using a 2D or 3D chart is not practical and is sometimes impossible. Segmentation with three or more variables is more complicated because it is not possible to plot them.

K-means clustering helps us to find the optimal number of segments or groups for our data. The best case is to have segments that are as compact as possible.

Each segment has a mean, or centroid, and its values are supposed to be as close as possible to the centroid. This means that the standard deviation of each segment must be as small as possible.

You need to pay attention to segments with large standard deviations because they could be outliers. This type of value in our dataset could mean a preview for future problems because they have a random and irregular behavior outside the rest of the data's normal execution.

In the next chapter, we will get an introduction to the linear regression supervised machine learning algorithm. Linear regression needs statistical tests for the data to measure its level of relationship and to check whether it is useful for the model. Otherwise, it is not worth building the model.