Data Forecasting and Segmentation Using Microsoft Excel: Perform data grouping, linear predictions, and time series machine learning statistics withou

Name: Data Forecasting and Segmentation Using Microsoft Excel: Perform data grouping, linear predictions, and time series machine learning statistics withou
Brand: Atlantic Books
SKU: 9781803247731
Price: 4558.0 INR
Availability: InStock

by Fernando Roque

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ISBN13: 9781803247731
Binding: Paperback
Subject: N/A
Publisher: Packt Publishing
Publisher Imprint: Packt Publishing
Publication Date: May 27, 2022
Pages: 324
Original Price: USD 48.99
Language: English
Edition: N/A
Item Weight: 558 grams
BISAC Subject(s): Data Science / Data Modeling & Design

About The Book

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.

About The Author

Roque, Fernando: - 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.

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