#### 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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# Training the model

We are going to use sales data of quarters for the years 2012 to 2015 to design and train the forecast time-series model. With this design, we will test the predictions with a group of known sales for 2016 to 2017. Finally, we will make a forecast for 2018 to 2019.

The model has to take both of these components (known sales and forecast) to make a good prediction. The steps to develop a forecast, as we will see in this chapter, are as follows:

1. Look at the data chart to decide whether it has autocorrelation or not.
2. Test the autocorrelation with the Durbin-Watson test.
3. Calculate the moving average (explained in Chapter 12, Working with Time Series Using the Centered Moving Average and a Trending Component) to smooth the peaks of the data.
4. Design the model, calculating the seasonal trends.
5. Test the forecast by multiplying the seasonal trend by the regression line.
6. Use the model to make forecasts.

In this chapter, we will design...