#### 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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# Doing the forecast

To build the forecast, we follow the same steps that we saw earlier when testing the model:

1. Get the seasonal trend for each quarter from 2018 to 2019.
2. Calculate the trend for 2018 to 2019 using the regression line.
3. Forecast by multiplying the seasonal trend by the regression.

The forecast chart reveals that car sales keep growing, following the trend with a season fall in the fourth quarter. We see that the data has two components:

• A seasonal component with the sales fluctuation amount quarters
• A trend component that indicates the sales growth each year

To do the forecast, we calculate the seasonal trend and the regression trend. The highlighted data in Figure 13.8 shows the calculations to apply the model to make a forecast for the years 2018 and 2019:

Figure 13.8 – Using the forecast for automobile sales for 2018–2019

The forecast model makes a good prediction for the years 2018...