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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Testing the forecast model

We will see the forecast fit using the car sales from 2016 to 2017. This data is known from the dataset, so we will be able to check whether the model works well or not.

Follow these steps to test the model:

1. Copy and paste from the previous data the seasonal trend for each quarter from 2016 to 2017.
2. Calculate the trend for 2016 to 2017 using the regression line.
3. Test the forecast by multiplying the seasonal trend by the regression.

Use the known data of sales for the years 2016 and 2017 to test the forecast model, as mentioned in step 3 of the previous list. The automobile sales are in the highlighted QuarterTot column in Figure 13.7. We use this data to see whether the model is useful to predict this information.

The season trend per quarterly period for the years 2016 and 2017 is the same past data we have seen before. Calculate the regression trend for the 2016–2017 quarters. This data is highlighted in Figure 13...