Data Forecasting and Segmentation Using Microsoft Excel | Perform data grouping, linear predictions, and time series machine learning statistics without using code

Name: Data Forecasting and Segmentation Using Microsoft Excel | Perform data grouping, linear predictions, and time series machine learning statistics without using code
Author: Fernando Roque
ISBN: 9781803247731

Fernando Roque

Lingua: inglese

Editore: Packt Publishing, 2022

1803247738 / 9781803247731

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Titolo: Data Forecasting and Segmentation Using Microsoft Excel | Perform data grouping, linear predictions, and time series machine learning statistics without using code
Autore: Fernando Roque
Editore: Packt Publishing
Anno di pubblicazione: 2022
Condizione: Neu
Rilegatura: Taschenbuch
Lingua: inglese
ISBN 10: 1803247738
ISBN 13: 9781803247731
Peso dell'articolo: 607 grammi
Dimensioni: 235 x 191 x 18 mm
Cataloghi dei venditori: Bücher

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.

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

"Riassunto" può appartenere a un’altra edizione di questo titolo.

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