Data Forecasting and Segmentation Using Microsoft Excel: Perform data grouping, linear predictions and time series ML statistics

Data Forecasting and Segmentation Using Microsoft Excel: Perform data grouping, linear predictions and time series ML statistics

English | 2022 | ISBN: ‎ 1803247738 | 325 pages | True PDF EPUB | 30.56 MB

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 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 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

Understanding Data Segmentation
Applying Linear Regression
What is Time Series?
An Introduction to Data Grouping
Finding the Optimal Number of Single Variable Groups
Finding the Optimal Number of Multi-Variable Groups
Analyzing Outliers for Data Anomalies
Finding the Relationship between Variables
Building, Training, and Validating a Linear Model
Building, Training, and Validating a Multiple Regression Model
Testing Data for Time Series Compliance
Working with Time Series Using the Centered Moving Average and a Trending Component
Training, Validating, and Running the Model

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