Publications by Brian Singh
BrianSingh_Data624_FinalProj
Data 624 Final Project This is role playing. I am your new boss. I am in charge of production at ABC Beverage and you are a team of data scientists reporting to me. My leadership has told me that new regulations are requiring us to understand our manufacturing process, the predictive factors and be able to report to them our predictive model of PH....
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BrianSingh_Data624_HW10
Imagine 10000 receipts sitting on your table. Each receipt represents a transaction with items that were purchased. The receipt is a representation of stuff that went into a customer’s basket - and therefore ‘Market Basket Analysis’. That is exactly what the Groceries Data Set contains: a collection of receipts with each line representing...
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BrianSingh_Data624_HW9
library(AppliedPredictiveModeling) ## Warning: package 'AppliedPredictiveModeling' was built under R version 4.2.3 library(ipred) ## Warning: package 'ipred' was built under R version 4.2.3 library(dplyr) ## Warning: package 'dplyr' was built under R version 4.2.3 ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:...
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BrianSingh_Data624_HW8
##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 = 10 sin(πx1x2) + 20(x3 − 0.5)2 + 10x4 + 5x5 + N(0, σ2) where the x values are random variables uniformly distributed between [0, 1] (there are also 5 other non-informative varia...
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BrianSingh_Data624_HW7
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: (a) Start R and use these commands to load the data: library(AppliedPredictiveModeling) ## Warning: package 'App...
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BrianSingh_Data624_Project1
library(fpp3) ## ── Attaching packages ────────────────────────────────────────────── fpp3 0.5 ── ## ✔ tibble 3.2.1 ✔ tsibble 1.1.4 ## ✔ dplyr 1.1.3 ✔ tsibbledata 0.4.1 ## ✔ tidyr 1.3.0 ✔ feasts 0.3.1...
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BrianSingh_Data624_HW6
9.1 Figure 9.32 shows the ACFs for 36 random numbers, 360 random numbers and 1,000 random numbers. a. Explain the differences among these figures. Do they all indicate that the data are white noise? Per Hyndman, for white noise series, we expect each autocorrelation to be close to zero and mostly in bounds of the dotted lines. This is the case...
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BrianSingh_Data624_HW5
library(fpp3) ## ── Attaching packages ────────────────────────────────────────────── fpp3 0.5 ── ## ✔ tibble 3.2.1 ✔ tsibble 1.1.4 ## ✔ dplyr 1.1.3 ✔ tsibbledata 0.4.1 ## ✔ tidyr 1.3.0 ✔ feasts 0.3.1...
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BrianSingh_Data624_HW4
library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union library(ggplot2) library(lattice) library(psych) ## ## Attaching package: 'psych' ## The following objects are mask...
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BrianSingh_Data624_HW3
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) library(fpp3) ## Warning: package 'fpp3' was built under R version 4.2.3 ## ── Attaching packages ──────────────────────�...
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