#### 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 and testing the model

To predict new values using the data that we have, we will follow these steps:

• Use 80% percent of the data to train and generate the linear regression model.
• Give the upper and lower models ranges of uncertainty. Remember that a model is just a trend and approximation of the prediction values.
• Test the linear model with the remaining 20% of the data and see how the model fits with these expected values.
• Use the model to predict new values using unknown data.

Build the linear model formula with 80% of the data in Figure 10.19:

Figure 10.19 – Linear regression coefficients for building a formula model

## Writing a linear regression model formula

Using the coefficients of the linear model, we build the formula for the regression line:

```Intercept B0 = -34.8789
B1 material rotation = 0.63
B2 online marketing = 15.699
Predicted sales revenue from model = Intercept B0 + (B1 * material rotation)...```