finelybook
Data Forecasting and Segmentation Using Microsoft Excel: Perform data grouping, linear predictions, and time series machine learning statistics without using code
Author: Fernando Roque
Publisher Finelybook 出版社: Packt Publishing (May 27, 2022)
Language 语言: English
pages 页数: 324 pages
ISBN-10 书号: 1803247738
ISBN-13 书号: 9781803247731
Book Description
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
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. Author: analyzing groups returned Author: K-means, you’ll be able to detect outliers that could indicate possible fraud or a bad function in network packets.
Author: 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
