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Hands-On Machine Learning with Microsoft Excel 2019

You're reading from Hands-On Machine Learning with Microsoft Excel 2019

Product type Book

Published in Apr 2019

Publisher Packt

ISBN-13 9781789345377

Pages 254 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Julio Cesar Rodriguez Martino

Table of Contents (17) Chapters

Preface

Section 1: Machine Learning Basics

Implementing Machine Learning Algorithms

Hands-On Examples of Machine Learning Models

Section 2: Data Collection and Preparation

Importing Data into Excel from Different Data Sources

Data Cleansing and Preliminary Data Analysis

Correlations and the Importance of Variables

Section 3: Analytics and Machine Learning Models

Data Mining Models in Excel Hands-On Examples

Implementing Time Series

Section 4: Data Visualization and Advanced Machine Learning

Visualizing Data in Diagrams, Histograms, and Maps

Artificial Neural Networks

Azure and Excel - Machine Learning in the Cloud

The Future of Machine Learning

Assessment

Chapter 7, Implementing Time Series

By setting increasing(TravelDate) to the moving average values in the calculation and following the same steps.
If the seasonality is too different from the real value in the data, then the prediction will have less accuracy. If we increase the confidence interval, then the error will also increase.
Using the COVARIANCE.P function in Excel.

The time series diagram, after applying the logarithm, will look like the following screenshot:

The trend is still ascending, but the standard deviation looks flat and is not dependent on the time.

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Authors (1)

Julio Cesar Rodriguez Martino

Julio Cesar Rodriguez Martino is a machine learning (ML) and artificial intelligence (AI) platform architect, focusing on applying the latest techniques and models in these fields to optimize, automate, and improve the work of tax and accounting consultants. The main tool used in this practice is the MS Office platform, which Azure services complement perfectly by adding intelligence to the different tasks. Julio's background is in experimental physics, where he learned and applied advanced statistical and data analysis methods. He also teaches university courses and provides in-company training on machine learning and analytics, and has a lot of experience leading data science teams.

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