Publications by Shane Hylton
DATA 624 Final Project
DATA 624 Final Project Shane Hylton 2023-05-15 data <- read_excel("StudentData.xlsx") eval <- read_excel("StudentEvaluation.xlsx") eval_final <- eval target_PH <- eval$PH summary(data) ## Brand Code Carb Volume Fill Ounces PC Volume ## Length:2571 Min. :5.040 Min. :23.63 Min. :0.07933 ## Class :charact...
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DATA 624 Assignment 9
DATA 624 Assignment 9 Shane Hylton 2023-05-02 8.1 A. library(mlbench) set.seed(34) simulated <- mlbench.friedman1(200, sd = 1) simulated <- cbind(simulated$x, simulated$y) simulated <- as.data.frame(simulated) colnames(simulated)[ncol(simulated)] <- "y" model1 <- randomForest(y~.,data=simulated, importance = TRUE,ntree = 1000) rfImp1 <- varImp(...
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DATA 624 Assignment 8
DATA 624 Assignment 8 Shane Hylton 2023-04-23 7.2 library(mlbench) set.seed(34) trainingData <- mlbench.friedman1(200, sd = 1) trainingData$x ## [,1] [,2] [,3] [,4] [,5] ## [1,] 0.444768541 0.0491703867 0.368916968 0.09420248 0.9574439991 ## [2,] 0.998540404 0.7377619252 0.342259319 0.90469829 0.0902...
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DATA 624 Assignment 7
DATA 624 Assignment 7 Shane Hylton 2023-04-02 6.2 A and B data(permeability) fingerprints <- fingerprints fingerprints_trim <- fingerprints[, -nearZeroVar(fingerprints)] fingerprints_df <- data.frame(fingerprints) fingerprints_trim_df <- data.frame(fingerprints_trim) The trimmed data leaves 388 variables, as opposed to the original 1107. C set....
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DATA 624 Project 1
DATA 624 Project 1 Shane Hylton 2023-03-26 Part 1 Question: In part A, I want you to forecast how much cash is taken out of 4 different ATM machines for May 2010. The data is given in a single file. The variable ‘Cash’ is provided in hundreds of dollars, other than that it is straight forward. I am being somewhat ambiguous on purpose to make ...
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DATA 624 Assignment 6
DATA 624 Assignment 6 Shane Hylton 2023-03-19 9.1 A: Each subsequent lag plot appears as though a round of differencing was applied. Though they are simply representations of the distribution of white noise for increasingly larger sample sizes, they resemble what a lag plot would look like for a dataset that underwent differencing. The spread in ...
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ARIMA
ARIMA Example Shane Hylton and Raymond Fries 2023-03-06 Visualization and Transformation Differencing (Attaining Stationarity) Time Series Display of First Order Differencing Time Series Display of Second Order Differencing Model Creation and Evaluation Parameters of Each Model Model p d q arima012 0 1 2 arima110 1 1 0 stepwise ...
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DATA 624 Assignment 5
DATA 624 Assignment 5 Shane Hylton 2023-03-06 8.1, 8.5, 8.6, 8.7, 8.8, 8.9 8.1 aus_livestock <- aus_livestock vic_pig <- subset(aus_livestock,State =="Victoria" & Animal == "Pigs") vic_pig <- vic_pig %>% select(Month,Count) %>% mutate(Month = yearmonth(Month)) %>% as_tsibble(index=Month) fit <- vic_pig %>% model(ETS(Count)) report(fit...
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DATA 624 Assignment 4
DATA 624 Assignment 4 Shane Hylton 2023-02-27 3.1 A data(Glass) glass <- data.frame(Glass) numeric_cols <- glass[-c(10)] numeric_cols %>% gather() %>% ggplot(aes(value,color = key))+ geom_histogram(bins=25)+ facet_wrap(~key,scales='free') numeric_cols %>% gather() %>% ggplot(aes(value,color = key))+ geom_boxplot()+ facet_wrap...
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DATA 624 Assignment 3
DATA 624 Assignment 3 Shane Hylton 2023-02-21 5.1, 5.2, 5.3, 5.4 and 5.7 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 ) Bricks ( aus_production ) NSW Lambs ( aus_livestock ) Household wealth ( hh_budget ). Australi...
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