Publications by U.S. Geological Survey/ The Nature Conservancy
DATA624 Assignment 6
8.1, 8.2, 8.3, 8.5., 8.6, 8.7 8.1 Figure 8.31 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? Each series is showing the ACF function of a random time series. Each is indicating white noise since correlations a...
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DATA624 Assignment 4
3.1. The UC Irvine Machine Learning Repository 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 Glass data set was loaded, and some ...
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DATA624 Assignment 5
7.1 Consider the pigs series — the number of pigs slaughtered in Victoria each month. This time series does not seem to follow a trend or have a pronounced seasonality, so simple exponential smoothing is a reasonable model to run. str(pigs) ## Time-Series [1:188] from 1980 to 1996: 76378 71947 33873 96428 105084 ... (pigs) %>% autoplot Use th...
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DATA624_Project2
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. Please use the histo...
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DATA624 Assignment 8
7.2 Data prep library(mlbench) library(caret) set.seed(200) # for reproducibility # train and test set data are setup in accordance with Kuhn and Johnson provided code trainingData <- mlbench.friedman1(200, sd=1) trainingData$x <- data.frame(trainingData$x) featurePlot(trainingData$x,trainingData$y) testData <- mlbench.friedman1(5000, s...
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DATA624 Assignment 9
8.1 Random Forest Prep Data Recreate the simulated data from Exercise 7.2: library(rpart) library(randomForest) library(caret) library(tidyverse) library(gbm) library(mlbench) library(Cubist) library(gbm) set.seed(200) simulated <- mlbench.friedman1(200,sd=1) simulated <- cbind(simulated$x, simulated$y) simulated <- as.data.frame(simul...
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DATA622_final.project_rjw
Functions & Packages The first function sets panel, axis, legend, and strip properties to all calls to ggplot in the rest of the document the remaining functions are meant to integrate all models for purposes of visualizing and comparing results library(tidyverse) #library(keras) library(magrittr) library(ggplot2) library(caret) ## 1. Fun...
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