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 learned how to build a linear regression formula and, beyond this, how to visualize the distances between expected values and a model. These distances are input for statistical tests to find out whether the model is good enough to predict new values.

The machine learning workflow to use a model for prediction starts by doing a definition of the target information we expect and data validation, using a chart to see the possible relationships between the variables. We use 80% of the known data to train the model and see whether it returns values that make sense to our experience. With the remaining 20% of the data, we test the model and see whether it fits the data that was not part of the training. Finally, we predict new values. We have to apply our judgment to see whether the regression is working or not.

This knowledge is useful to apply statistical tests that reject the null hypothesis that the slope of the linear model is equal to zero. A slope...