#### 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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# Summary

The forecast calculation depends entirely on the quality of autocorrelation in the data. If present values are dependent on past data, then the data will give good future predictions for the time series. The Durbin-Watson probe to check the level of autocorrelation of the time series tells us how good the prediction will be by measuring the influence of past data on the current values.

The season component depends on the CMA distance to the data. The season component is determined by the forecast as a factor to move the trend (linear regression) up or down, depending on the cycles of the time series. Comparing the forecast time-series line chart with the original data gives us an idea of how accurate the model's prediction is.

In this chapter, we learned to use the CMA to smooth the peaks and troughs of the seasonal values over the years. The CMA helps to calculate the seasonal trend weight that leads the regression line up and down for the monthly dependable forecasting...