Data Forecasting and Segmentation Using Microsoft Excel : Perform data grouping, linear predictions, and time series machine learning statistics without using code
Language: English
Published by Packt Publishing, 2022
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
US$ 89.37
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Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - 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. …
Seller Inventory # 9781803247731
- Title
- 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
- Packt Publishing
- Publication year
- 2022
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1803247738
- ISBN 13
- 9781803247731
- Item weight
- 607 grams
- Dimensions
- 235x191x18 mm
Perform time series forecasts, linear prediction, and data segmentation with no-code Excel machine learning
Purchase of the print or Kindle book includes a free PDF eBook
What’s inside
- Techniques to segment data, perform regression predictions, and time series forecasts without writing any code
- A guide to grouping multiple variables with K-means using Excel plugin without programming
- Easy-to-follow instructions on building, validating, and predicting with a multiple linear regression model and time series forecasts
You’ll get the most out of this book if
- You're a data analyst, a business analyst, or a data science professional
- You work in MIS, finance, or auditing and have a solid grasp on MS Excel
What your journey will look like
This book guides you through basic statistics to test whether your data can be used to perform regression predictions and time series forecasts. The exercises 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 to find segments of your data that are impossible to see with other analyses. 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, you'll be able to use the classification algorithm to group data with different variable and train linear and time series models to perform predictions and forecasts based on past data.
Some of the things you’ll learn from this book
- The importance of machine learning for classifying data segmentation
- How to perform basic statistics tests for regression variable dependency
- Test time series autocorrelation to build a useful forecast
- Using Excel add-ins to run K-means without programming
- Segment outlier analysis for possible data anomalies and fraud
- Building, training, and validating multiple regression models and time series forecasts
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
"Synopsis" may belong to another edition of this title.
About the Author
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.
"About the title" may belong to another edition of this title.
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