Data Forecasting and Segmentation Using Microsoft Excel: Perform data grouping, linear predictions, and time series machine learning statistics withou (Paperback or Softback)

Roque, Fernando

ISBN 10: 1803247738 / ISBN 13: 9781803247731

Edité par Packt Publishing 5/27/2022, 2022

A propos de cet article

Data Forecasting and Segmentation Using Microsoft Excel: Perform data grouping, linear predictions, and time series machine learning statistics withou.

N° de réf. du vendeur BBS-9781803247731

Détails bibliographiques

• Titre: Data Forecasting and Segmentation Using Microsoft Excel: Perform data grouping, linear predictions, and time series machine learning statistics withou (Paperback or Softback)
• Éditeur: Packt Publishing 5/27/2022
• Date de parution: 2022
• Langue: anglais
• ISBN à 10 chiffres: 1803247738
• ISBN à 13 chiffres: 9781803247731
• Reliure: Paperback or Softback
• État de l'article: New
• Poids: 1,23 livre
• Type de livre: Book

A propos de ce titre

Synopsis

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

1. Understanding Data Segmentation
2. Applying Linear Regression
3. What is Time Series?
4. An 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 a Linear Model
10. Building, Training, and Validating a Multiple Regression Model
11. Testing Data for Time Series Compliance
12. Working with Time Series Using the Centered Moving Average and a Trending Component
13. Training, Validating, and Running the Model

À propos de l?auteur

Fernando Roque has 24 years of experience working with statistics for quality control and financial risk assessment of projects since planning, budgeting, and execution. Fernando works applying python k-means and time-series machine-learning algorithms using vegetable activity (NDVI) drones’ images to find the crop´s region with more resilience to droughts. He also applies time-series and k-means for supply chain management (logistics) and inventory planning for seasonal demand.