Publications by Kory Martin
DATA 624 - Project 2
Introduction For this project, the focus is on looking to develop a model that allows us to predict the pH for our products and understand which factors are used in helping us to predict the output. In order to do this we will be looking at exploring different modeling techniques to try and predict the pH level. Given that we are looking to predict...
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DATA 624 - Homework 9
Problem 8.1 set.seed(200) simulated <- mlbench.friedman1(200, sd=1) simulated <- cbind(simulated$x, simulated$y) simulated <- as.data.frame(simulated) colnames(simulated)[ncol(simulated)] <- "y" Random Forest Model model1 <- randomForest(y ~ ., data=simulated, importance=TRUE, ntree =1000) rfImp1 <- varImp(model1, scale=FALSE) rfImp1 ## ...
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DATA 624 - Homework 8
Problem 7.2 Setup set.seed(200) training_data <- mlbench.friedman1(200, sd=1) training_data$x <- data.frame(training_data$x) featurePlot(training_data$x, training_data$y) test_data <- mlbench.friedman1(5000,sd=1) test_data$x <- data.frame(test_data$x) Model 1 - KNN knnModel <- train(x = training_data$x, y = training_data$y,...
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DATA 624 - Homework 7
Problem 6.1 Part a library(caret) data(tecator) raw_data <- as_tibble(absorp) ## Warning: The `x` argument of `as_tibble.matrix()` must have unique column names if ## `.name_repair` is omitted as of tibble 2.0.0. ## ℹ Using compatibility `.name_repair`. ## This warning is displayed once every 8 hours. ## Call `lifecycle::last_lifecycle_warnings...
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DATA624_Project1_PartA
Overview In part A of this project, our goal is to forecast how much cash is taken out of 4 different ATM machines for May 2010. For this project, we will conduct the following steps. We will start by importing the raw data for each of the datasets into a dataframe, and handle any initial cleanup or conversions of the data Next we will explore the...
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DATA 624_Project1_PartC
Overview: Part C consists of two data sets. These are simple 2 columns sets, however they have different time stamps. Your optional assignment is to time-base sequence the data and aggregate based on hour (example of what this looks like, follows). Note for multiple recordings within an hour, take the mean. Then to determine if the data is stationa...
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DATA624_Project1_PartB
Overview In Part B of this project, we have a dataset of residential power usage for January 1998 until December 2013. The goal is to develop a model these data and provide a monthly forecast for 2014. The general process, will be the same as outlined in Part A Import data and convert to tsibble We begin by importing the data. Once we have the dat...
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Publish Document
Overview In part A of this project, our goal is to forecast how much cash is taken out of 4 different ATM machines for May 2010. For this project, we will conduct the following steps. We will start by importing the raw data for each of the datasets into a dataframe, and handle any initial cleanup or conversions of the data Next we will explore the...
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DATA 624 - Homework #6
Problem 9.1 Explain the differences among these figures. Do they all indicate that the data are white noise? For each of the graphs, the data appears to be white-noise, based on the fact that non of the lags are in the statistially significant region. Why are the critical values at different distances from the mean of zero? Why are the autocorre...
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DATA 624 - Homework #5
DATA 621 - Homework 5 Problem 8.1 Consider the the number of pigs slaughtered in Victoria, available in the aus_livestock dataset. Use the ETS() function to estimate the equivalent model for simple exponential smoothing. Find the optimal values of α and ℓ 0 , and generate forecasts for the next four months. Compute a 95% prediction interval for...
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