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  • Book Overview & Buying R for Data Science
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R for Data Science

R for Data Science

By : Toomey
3.4 (5)
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R for Data Science

R for Data Science

3.4 (5)
By: Toomey

Overview of this book

If you are a data analyst who has a firm grip on some advanced data analysis techniques and wants to learn how to leverage the features of R, this is the book for you. You should have some basic knowledge of the R language and should know about some data science topics.
Table of Contents (14 chapters)
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Chapter 11. Predicting Events with Machine Learning

R programming has several tools that can be used when dealing with events in a time series. We can look at the time series from several aspects, evaluate the components involved in the data, construct a model of the time series behavior, and estimate or forecast time series events going forward.

This chapter covers the analysis of time series data with the objective of forecasting. There are several areas in R programming that can be used for time series forecasting:

  • Converting your data into an R-formatted time series
  • Examining seasonality effects
  • Simple smoothing
  • Basic trend analysis, including decomposing your time series into seasonal, trend, and irregular components
  • Exponential smoothing, including Holt-Winters filtering, correlogram, and box test
  • ARIMA modeling
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Tech Concepts
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Programming languages
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R for Data Science
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