Publications by William Aiken
DATA624 Project2-Technical Report
Project Introduction: New regulations are requiring ABC Beverage to provide a report with an outline of our manufacturing process, and a predictive model of PH including an explanation of predictive factors. Our data science team is tasked with developing the predictive model from provided historical data and using that model to predict PH on te...
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Data625=Project2-V5
Project Introduction: New regulations are requiring ABC Beverage to provide a report with an outline of our manufacturing process, and a predictive model of PH including an explanation of predictive factors. Our data science team is tasked with developing the predictive model from provided historical data and using that model to predict PH on te...
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DATA624 Project 2-Collinearity Assessment, Feature Selection and MARS Models
title: “Project 2” author: “Deepa Sharma/William Aiken” date: “2024-11-13” output: html_document Data Acquisition Here we can preview the data structure: student_train = read.csv('https://raw.githubusercontent.com/deepasharma06/Data-624/refs/heads/main/StudentData_training.csv') student_eval = read.csv('https://raw.githubusercontent....
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DATA624 HW9
Exercise 8.1. Recreate the simulated data from Exercise 7.2: library(mlbench) 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" (a) Fit a random forest model to all of the predictors, then estimate the variable imp...
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DATA624 HW8
R Markdown Exercise 7.2. Friedman (1991) introduced several benchmark data sets created by simulation. One of these simulations used the following nonlinear equation to create data: \(y = 10sin(\pi x_1x_2)+20(x_3 -0.5)^2 +10x_4 +5x_5 +N(0,\sigma^2)\) where the x values are random variables uniformly distributed between [0, 1] (there are also 5 oth...
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DATA624 HW7
Exercise 6.2 Developing a model to predict permeability (see Sect. 1.4) could save significant resources for a pharmaceutical company, while at the same time more rapidly identifying molecules that have a sufficient permeability to become a drug: Start R and use these commands to load the data: library(AppliedPredictiveModeling) data(permeability...
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DATA624 Project 1
Part A – ATM Forecast, ATM624Data.xlsx 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 this have a little mo...
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DATA624 HW6
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 difference between this figures is that the ACF of the lag decreases as the sample of random numbers increases. With 36 numbers their might...
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DATA624 HW5
Exercise 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 ℓ0, and generate forecasts for the next four months. The optimal values of alpha is 0.32 and the optimal value f...
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DATA624 HW4
Exercise 3.1 The UC Irvine Machine Learning Repository6 contains a data set related to glass identification. The data consist of 214 glass samples labeled as one of seven class categories. There are nine predictors, including the refractive index and percentages of eight elements: Na, Mg, Al, Si, K, Ca, Ba, and Fe. I couldn’t get this ‘Glass’...
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