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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Preface

This book is about giving you basic statistical knowledge to work with machine learning using complex algorithms to classify data, such as the K-means method. You will use an included add-in for Excel to practice the concepts of grouping statistics without the need for a deep programming background in the R language or Python.

The book covers three topics of machine learning:

• Data segmentation
• Linear regression
• Forecasts with time series

Data segmentation has many practical applications because it allows applying different strategies depending on the segment data ranges. It has applications in marketing and inventory rotation to act accordingly to the location and season of the sales.

The linear regression statistical concepts in this book will help you to explore whether the variables that we are using are useful to build a predictive model.

The time series model helps to do a forecast depending on the different seasons of the year. It has applications in inventory planning to allocate the correct quantities of products and avoid stalled cash flow in the warehouses. The time series depends on statistical tests to see whether the present values depend on the past, so they are useful to forecast the future.