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Data forecasting and segmentation using Microsoft Excel : perform data grouping, linear predictions, and time series machine learning statistics without using code /
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, valida...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing Limited,
2022.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 245 | 1 | 0 | |a Data forecasting and segmentation using Microsoft Excel : |b perform data grouping, linear predictions, and time series machine learning statistics without using code / |c Fernando Roque. |
| 264 | 1 | |a Birmingham : |b Packt Publishing Limited, |c 2022. | |
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| 520 | |a 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. | ||
| 505 | 0 | |a 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. | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
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| 650 | 7 | |a Commercial statistics |2 fast | |
| 650 | 7 | |a Industrial management |x Statistical methods |2 fast | |
| 650 | 7 | |a Statistics |2 fast | |
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