Data Forecasting and Segmentation Using Microsoft Excel

Perform time series forecasts, linear prediction, and data segmentation with no-code Excel machine learning

Key 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 forecasts

Book Description

Data Forecasting and Segmentation Using Microsoft Excel guides you through basic statistics to test if your data can be used to perform regression predictions and time series forecasts. The exercises covered in the 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 that helps you find segments of your data that are impossible to see with other analyses like business intelligence (BI) and pivot analysis. By analyzing groups returned by K-means, you'll be able to detect the outliers that could indicate possible fraud or a bad function in the network packets.

By the end of this book, you'll be able to use the classification algorithm to group the 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 frauds
• Build, train, and validate multiple regression models and time series forecasts

Who This Book Is For

This 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

1. Understanding Data Segmentation
2. Applying Linear Regression
3. What is Time Series?
4. Introduction to Data Grouping
5. Finding the Optimal Number of Single Variable Groups
6. Finding the Optimal Number of Multi Variable Groups
7. Analyzing Outliers for Data Anomalies
8. Finding the Relationship between Variables
9. Building, Training, and Validating the Linear Model
10. Building, Training, and Validating Multiple Regression Models
11. Testing Data for Time Series Compliance
12. Working with the ARIMA and SARIMA Function
13. Training, Validating, and Running the Model