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.
publication date
2022-05-27
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
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
approach
This book works with three major aspects of Machine Learning, grouping, regression, and time-series using Excel and without programming code. It covers basic statistics to understand the regression model variables dependency and time-series autocorrelation to build useful predictions and forecasts.
audience
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.
short description
This book uses real-life datasets from Kaggle to explain basic statistics for machine learning for data segmentation, regression predictions, and forecasts. You’ll focus on variable dependency and autocorrelation to build, test, and use a linear regression prediction model and time series forecasts.
subtitle
Perform data grouping, linear predictions, and time series machine learning statistics without using code
keywords
Data Forecasting, Data Segmentation, Microsoft Excel, Machine Learning with Excel
Product ISBN
9781803247731
