Publications by Glen Dale Davis
DATA 624 - Homework 9
Packages: library(tidyverse) library(mlbench) library(randomForest) library(caret) library(AppliedPredictiveModeling) library(knitr) library(party) library(gbm) library(Cubist) library(rpart) Exercise 8.1: Recreate the simulated data from Exercise 7.2: set.seed(200) simulated <- mlbench.friedman1(200, sd = 1) simulated <- cbind(simulate...
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DATA624 - Homework 8
Packages: library(caret) library(tidyverse) library(RColorBrewer) library(knitr) library(cowplot) library(mlbench) library(earth) library(kernlab) library(rminer) library(AppliedPredictiveModeling) Exercise 7.2: Friedman (1991) introduced several benchmark data sets created by simulation. One of these simulations used the following nonli...
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DATA 624 - Homework 7
Packages: library(caret) library(tidyverse) library(RColorBrewer) library(knitr) library(pracma) library(cowplot) library(AppliedPredictiveModeling) library(elasticnet) library(glmnet) library(VIM) Exercise 6.2: Developing a model to predict permeability (see Sect. 1.4) could save significant resources for a pharmaceutical company, while...
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Data 624 - Project 1
Packages: library(fpp3) library(RColorBrewer) library(knitr) library(pracma) library(cowplot) library(readxl) library(httr) library(writexl) Part A: Data Preparation: We load transaction data for four ATMS from May 2009 to April 2010. my_url <- "https://github.com/geedoubledee/data624_project1/raw/main/ATM624Data.xlsx" col_types <- c("da...
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DATA624 - Homework 6
Packages: library(fpp3) library(RColorBrewer) library(knitr) library(pracma) library(cowplot) Exercise 9.1: Figure 9.32 shows the ACFs for 36 random numbers, 360 random numbers and 1,000 random numbers. Explain the differences among these figures. Do they all indicate that the data are white noise? The differences in these figures can be e...
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Data621 - HW2
library(tidyverse) library(geomtextpath) Overview: In this homework assignment, you will work through various classification metrics. You will be asked to create functions in R to carry out the various calculations. You will also investigate some functions in packages that will let you obtain the equivalent results. Finally, you will create g...
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Data 624 - Homework 5
library(fpp3) library(RColorBrewer) library(knitr) library(pracma) palette <- brewer.pal(n = 11, name = "BrBG") #function taken from here: https://bookdown.org/yihui/rmarkdown-cookbook/font-color.html colorize <- function(x, color){ if (knitr::is_latex_output()) { sprintf("\\textcolor{%s}{%s}", color, x) } else if (knitr::i...
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DATA624 - Homework 4
library(fpp3) library(knitr) library(mlbench) library(ggcorrplot) library(RColorBrewer) library(MASS) select <- dplyr::select library(hablar) library(VIM) Exercise 3.1: The UC Irvine Machine Learning Repository contains a data set related to glass identification. The data consist of 214 glass samples labeled as one of seven class categor...
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Data 624 - Homework 3
library(fpp3) library(knitr) Exercise 5.1: Produce forecasts for the following series using whichever of NAIVE(y), SNAIVE(y) or RW(y ~ drift()) is more appropriate in each case: Australian Population (global_economy): Because the data is not very seasonal, and there is a clear upward trend in the data, the Drift forecast method is preferable...
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DATA 624 - Homework 2
library(fpp3) library(knitr) library(seasonal) library(cowplot) library(pracma) library(geoR) Exercise 3.1: Consider the GDP information in global_economy. Plot the GDP per capita for each country over time. Which country has the highest GDP per capita? How has this changed over time? Since there are so many countries (and non-countries) in...
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