Publications by rachelgreenlee
Final Project
Introduction This project is an exploration of how the public and/or shareholders can pressure companies to not turn a blind eye to the affects their operations have on the environment and the climate crisis. Many governments have been slow to impose sweeping regulations on business and industry with regards to harmful emissions, so looking to ho...
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DATA607 - Assignment 10
Purpose For this assignment I’ll be using the base code from Chapter 2 of Text Minint with R (https://www.tidytextmining.com/sentiment.html) to learn how to do some basic sentiment analysis with provided lexicons. Base code #gather the needed libraries, lexicons, and Jane Austen books as our corpus library(tidytext) library(textdata) librar...
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605 Final Project
Libraries library(tidyverse) library(ggcorrplot) library(matrixcalc) library(MASS) library(stats) Part 1 Using R, generate a random variable X that has 10,000 random uniform numbers from 1 to N, where N can be any number of your choosing greater than or equal to 6. Then generate a random variable Y that has 10,000 random normal numbers with...
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hw1_time-series-graphics
Exercises from Section 2 of Forecasting: Principles & Practice at https://otexts.com/fpp3/graphics-exercises.html Exercise 2.1 Use the help function to explore what the series gafa_stock, PBS, vic_elec and pelt represent. #?gafa_stock #?PBS #?vic_elec #?pelt Use autoplot() to plot some of the series in these data sets. autoplot(gafa_stock) ...
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hw2_time-series-decomposition
Exercises from Section 3 of Forecasting: Principles & Practice at https://otexts.com/fpp3/decomposition-exercises.html Exercise 3.1 Consider the GDP information in global_economy. Plot the GDP per capita for each country over time. Which country has the highest GDP per capita? How has this changed over time? For the most recent year of 2016 Mon...
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hw2_trees-and-forests
#prevent conflict with skimr and dlookr options(kableExtra.auto_format = FALSE) library(skimr) library(tidyverse) library(lubridate) library(rpart) #decision tree package rec'd by Practical ML in R textbook library(rpart.plot) #decision tree display package rec'd by Practical ML in R textbook library(randomForest) #for random forest model...
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hw6_ARIMA
Exercises from Section 9 of Forecasting: Principles & Practice at https://otexts.com/fpp3/arima-exercises.html Exercise 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? Technically yes, since al...
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hw5_exponential-smoothing
Exercises from Section 5 of Forecasting: Principles & Practice at https://otexts.com/fpp3/expsmooth-exercises.html Exercise 8.1 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...
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hw3_forecasting
Exercises from Section 5 of Forecasting: Principles & Practice at https://otexts.com/fpp3/toolbox-exercises.html Exercise 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) A quick plot of the data shows a fairly linea...
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hw4_preprocessing
Exercises from Chapter 3 of textbook Applied Predictive Modeling by Kuhn & Johnson Exercise 3.1 The UC Irvine Machine Learning Repository contains a data set related to glass identification. The data consit of 214 glass samples labeled as one of seven class categories. There are nine predictors, including the refractive index and percentages of...
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