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 introduction to time series forecasting, we learned that we need the data to autocorrelate to be useful for predictions. We have to plot the data and see whether it has seasonal or cyclical trends to learn whether past data influences the next period of data. Then, we need to use the Durbin-Watson statistical test to prove that the data is autocorrelated.

The forecast calculation uses the trending line of the regression model multiplied by the seasonal irregularity factor. This factor gives us the direction of the trending line based on the cyclical information of the past data. Use your experience to analyze whether the forecast returned by the model makes sense with the past data.

In the next chapter, we will start studying grouping statistics.