Publications by John K. Hancock
CUNY DATA 624 Homework7 - Linear Regression
Linear Regression - 6.3 (a) Start R and use these commands to load the data: library(AppliedPredictiveModeling) ## Warning: package 'AppliedPredictiveModeling' was built under R version 3.6.3 data(ChemicalManufacturingProcess) The matrix processPredictors contains the 57 predictors (12 describing the input biological material and 45 describing t...
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CUNY DATA 624 Solo Project One
Introduction The solo project consists of two parts. Part A tasks us with forecasting the amount of cash withdrawn from 4 ATM machines. Part B is forecasting residential power usage. The data provided for this project is in two excel files. The major outline of my approach is to examine the data, clean and prepare the data for analysis, and compa...
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DATA624 Spring 2021 - HW 6 - ARIMA
8.11.1 a. Figure 8.31 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? Starting with the definition of “white noise” which is “a time series that shows no autocorrelation”. Yes. All three random time series ...
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DATA 624 Spring 2021 HW4 - Data Preprocessing/Overfitting
Question No. 3.1 The Glass dataset consists of 214 observations and 10 variables. help(Glass) ## starting httpd help server ... done Glass Identification Database Description A data frame with 214 observation containing examples of the chemical analysis of 7 different types of glass. The problem is to forecast the type of class on basis of the...
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DATA624 Spring 2021 HW5 - Exponential Smoothing
7.8.1 Consider the pigs series – the number of pigs slaughtered in Victoria each month. a. Use the ses() function in R to find the optimal values of \(\alpha\) and \(\ell_0\) and generate forecasts for the next four months. First, we begin by inspecting the pigs time series by looking at its information, its head and tail. We see that the time...
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CUNY DATA624 Homework 8 - Non Linear Regression
7.2. Friedman (1991) introduced several benchmark data sets create 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 other non-informative v...
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CUNY DATA624 HW9 - Regression Trees and Rule Based Models
8.1 ##Recreate the simulated data from Exercise 7.2 library(mlbench) ## Warning: package 'mlbench' was built under R version 3.6.3 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" head(simulated) ## ...
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CUNY DATA 624 PROJECT TWO
OVERVIEW The data science team of Salma Elshahawy, John K. Hancock, and Farhana Zahir have prepared the following technical report to address the issue of understanding ABC’s manufacturing process and its predictive factors. This report is the predictive value of the PH. The report consists of the following: PART 1: THE DATASETS PART 2: DATA P...
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