Publications by Shamecca Marshall
Data 624 Project 1
1 DATA 624 Forecasting Project – ATM, Power Usage, and Water Flow Analysis 1.1 Introduction This project consists of three forecasting tasks. Part A involves predicting the cash withdrawals from four ATMs for May 2010. Part B focuses on forecasting residential power consumption for the year 2014 using monthly data from 1998–2013. Part C is...
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Data 622 0 Assignment 2
Introduction In Machine Learning, Experimentation refers to the systematic process of designing, executing, and analyzing different configurations to identify the optimal settings that performs best on a given task. Experimentation is learning by doing. It involves systematically changing parameters, evaluating results with metrics, and compari...
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Data 624 - HW 6
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? The difference between these figures lies in the height of the spikes and how wide the confidence bands are. As the sample size grows, the spi...
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Data 624 HW 5
knitr::opts_chunk$set(fig.width = 7, fig.height = 5, fig.align = "center") 8.1 Question: Consider the the number of pigs slaughtered in Victoria, available in the aus_livestock dataset. (a) Use the ETS() function to estimate the equivalent model for simple exponential smoothing. Find the optimal values of \(\alpha\) and \(\ell_0\), and generate...
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Data 622 - HW 1
Introduction This assignment focuses on Exploratory Data Analysis (EDA), a crucial step in data science that helps identify data gaps, imbalances, and provides insights into data quality. This analysis will focus on campaign effectiveness, customer demographics, and seasonality trends to understand how the bank can improve future marketing stra...
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Data 624 HW 4
3.1 The UC Irvine Machine Learning Repository contains a dataset related to glass identification. The dataset consists of 214 glass samples labeled as one of seven class categories. There are nine predictors, including the refractive index (RI) and percentages of eight elements: Na, Mg, Al, Si, K, Ca, Ba, and Fe. This dataset will be used to e...
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Data 624 - HW 3
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) global_economy %>% filter(Country == "Australia") %>% model(RW(Population ~ drift())) %>% forecast(h = 14) %>% autoplot(global_economy) + labs(title =...
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Data 624 HW1
1. Exploring Time Series Data Use the help function to explore what the series gold, woolyrnq, and gas represent. Load Required Libraries library(forecast) # ASSIGNING THE LIBRARY ## Warning: package 'forecast' was built under R version 4.4.2 ## Registered S3 method overwritten by 'quantmod': ## method from ## as.zoo.data.frame ...
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DATA 621 - HW #5
Problem Statement and Goals In this report, we generate a count regression model that is able to predict the number of cases of wine that will be sold given certain properties of the wine. The independent and dependent variables that are used in order to generate this model use data from 12,000 commercially available wines. The analysis detaile...
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Data 605 - Final Exam
Problem 1: Business Risk and Revenue Modeling Context You are a data scientist working for a retail chain that models sales, inventory levels, and the impact of pricing and seasonality on revenue. Your task is to analyze various distributions that can describe sales variability and forecast potential revenue. Part 1: Empirical and Theoretical ...
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