Publications by Gabriel Santos
DATA624 Project-1
Project - 1 Libraries Part A – ATM Forecast Part B – Forecasting Power Part C– BONUS, optional (part or all) Project - 1 This project consists of 3 parts - two required and one bonus and is worth 15% of your grade. Part A – ATM Forecast, ATM624Data.xlsx In part A, I want you to forecast how much cash is taken out of 4 different ATM mac...
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DATA624_HW6
library(fpp3) ## ── Attaching packages ────────────────────────────────────────────── fpp3 0.5 ── ## ✔ tibble 3.1.8 ✔ tsibble 1.1.3 ## ✔ dplyr 1.0.10 ✔ tsibbledata 0.4.1 ## ✔ tidyr 1.3.0 ✔ feasts ...
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DATA624_HW5
library(fpp2) library(fpp3) library(forecast) library(ggplot2) library(dplyr) 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 ℓ, and generate forecasts...
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DATA621_HW_1
knitr::opts_chunk$set(echo = FALSE, class.source = "codechunk") library(RCurl) library(tidyr) library(dplyr) library(RCurl) library(ggplot2) library(reshape2) library(corrplot) library(mice) library(car) library(reshape) library(mixtools) library(tidyverse) library(GGally) library(MASS) library(faraway) suppressPackageStartupMessag...
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DATA624_HW4
3.1 The UC Irvine Machine Learning Repository6 contains a data set related to glass identification. The data consist of 214 glass samples labeled as one of seven class categories. There are nine predictors, including the refractive index and percentages of eight elements: Na, Mg, Al, Si, K, Ca, Ba, and Fe. The data can be accessed via: library...
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DATA624_HW3
Load the following packages: tibble, for tibbles, a modern re-imagining of data frames. dplyr, for data manipulation. tidyr, to easily tidy data using spread() and gather(). lubridate, for date/times. ggplot2, for data visualization. tsibble, for tsibbles, a time series version of a tibble. tsibbledata, various time series data sets in the form of...
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DATA624_HW2
Exercise 3.1 Consider the GDP information in global_economy. Plot the GDP per capita for each country over time. Which country has the highest GDP per capita? How has this changed over time? View(global_economy) dim(global_economy) ## [1] 15150 9 sum(is.na(global_economy)) ## [1] 23678 max(global_economy$GDP) ## [1] NA max(global_economy$Pop...
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Playing around with the Tscount package
set.seed(1) require(forecast) library(tscount) library(ggplot2) library(parameters) library(tidyverse) library(nlme) library(scales) library(ggforce) library(kableExtra) 1 Spreading the word of temporal autocorrelation Events that occur in succession tend to be more similar than events that occur after a long separation. The extent t...
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DATA605 Week 11 Discussion Board
Using R, build a multiple regression model for data that interests you.Conduct residual analysis. Was the linear model appropriate? Why or why not?. Include in this model at least one quadratic term,bone dichotomous term, and one dichotomous vs. quantitative interaction term. Interpret all coefficients. For this discussion, I will look at th...
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Document test
Loading Libraries library(tidyverse) ## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ── ## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4 ## ✔ tibble 3.1.8 ✔ dplyr 1.0.10 ## ✔ tidyr 1.2.1 ✔ stringr 1.4.1 ##...
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