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

Chapter 8: Finding the Relationship between Variables

The linear regression algorithm is a supervised machine learning algorithm. We need to train and adjust the linear model before making predictions. We have to understand the data before applying linear regression to be sure that it will be useful for predictions.

You need a certain level of confidence that the variable you want to predict has a relationship with the variables that influence it. If you don't test the extent of this relationship, the predicted values will be errors, and the results will be garbage.

In this chapter, we will learn two methods to test the dependence of the variables to ensure our model's accuracy. We will measure the difference between the expected values from the training dataset and the model's results, and use statistical methods to examine the significance of the relationships between the variables to see whether they are useful for predicting values.

The goal of measuring...