Data Forecasting and Segmentation Using Microsoft Excel

Fernando Roque

• Publisher: Packt Publishing
• Year: 2022
• ISBN:
  9781803235264
• Edition: 1
• Topic: Computer Science
• Subtopic: Data Processing
• Index: Computer Science

Perform time series forecasts, linear prediction, and data segmentation with no-code Excel machine learningKey Features• Segment data, regression predictions, and time series forecasts without writing any code• Group multiple variables with K-means using Excel plugin without programming• Build, validate, and predict with a multiple linear regression model and time series forecastsBook DescriptionData 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.What you will learn• Understand why machine learning is important for classifying data segmentation• Focus on basic statistics tests for regression variable dependency• Test time series autocorrelation to build a useful forecast• Use Excel add-ins to run K-means without programming• Analyze segment outliers for possible data anomalies and fraud• Build, train, and validate multiple regression models and time series forecastsWho this book is forThis book is for data and business analysts as well as data science professionals. MIS, finance, and auditing professionals working with MS Excel will also find this book beneficial.

Table of contents

• Data Forecasting and Segmentation Using Microsoft Excel
• Contributors
• Preface
• Part 1 – An Introduction to Machine Learning Functions
• 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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