English | 2022 | ISBN: 1803247738| 325 pages | True PDF EPUB | 30.56 MB
Perform series forecasts, linear prediction, and data sntation with no-code Excel machine learning
Key Features
Snt data, regression predictions, and 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 series forecasts
Book Description
Data Forecasting and Sntation Using Microsoft Excel guides you through basic statistics to test whether your data can be used to perform regression predictions and 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 snts 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 series models to perform predictions and forecasts based on past data.
What you will learn
Understand why machine learning is important for classifying data sntation
Focus on basic statistics tests for regression variable dependency
Test series autocorrelation to build a useful forecast
Use Excel add-ins to run K-means without programming
Analyze snt outliers for possible data anomalies and fraud
Build, train, and validate multiple regression models and 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.
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