Publications by Vijaya Cherukuri
Data612-Project5
Objective The goal of this project is give you practice beginning to work with a distributed recommender system.It is sufficient for this assignment to build out your application on a single node. Adapt one of your recommendation systems to work with Apache Spark and compare the performance with your previous iteration. Consider the efficiency ...
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Data624_HW1
library(tidyverse) ## Warning: package 'tidyverse' was built under R version 3.6.2 ## -- Attaching packages -------------------------------------------------------------------------------- tidyverse 1.3.0 -- ## v ggplot2 3.2.1 v purrr 0.3.3 ## v tibble 3.0.3 v dplyr 1.0.2 ## v tidyr 1.1.0 v stringr 1.4.0 ## v readr 1.3.1 ...
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Data624_HW6
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? Series X1 has one, X2 has 4 and X3 has no autocorrelation outside the 95% limit. After observing the picture we can see none of the spikes are large...
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Data624_HW3
## Warning: package 'tidyverse' was built under R version 3.6.2 ## Warning: package 'tibble' was built under R version 3.6.3 ## Warning: package 'tidyr' was built under R version 3.6.3 ## Warning: package 'readr' was built under R version 3.6.3 ## Warning: package 'dplyr' was built under R version 3.6.3 ## Warning: package 'forcats' was built und...
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Data624_HW2
## Warning: package 'tidyverse' was built under R version 3.6.2 ## Warning: package 'tibble' was built under R version 3.6.3 ## Warning: package 'tidyr' was built under R version 3.6.3 ## Warning: package 'readr' was built under R version 3.6.3 ## Warning: package 'dplyr' was built under R version 3.6.3 ## Warning: package 'forcats' was built und...
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Data624_HW4
Question 3.1 The UC Irvine Machine Learning Repository contains a data set related to glass identification. The data consists of 214 glass samples labeled as one of several 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:...
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Data624_HW5
library(fpp2) ## Warning: package 'fpp2' was built under R version 3.6.3 ## Loading required package: ggplot2 ## Loading required package: forecast ## Warning: package 'forecast' was built under R version 3.6.3 ## Registered S3 method overwritten by 'quantmod': ## method from ## as.zoo.data.frame zoo ## Loading required package: ...
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Data624_Project1
Project 1 - This Project has 3 Parts - 2 required and 1 bonus #Load Required Libraries suppressMessages(suppressWarnings(library(fpp2))) suppressMessages(suppressWarnings(library(ggplot2))) suppressMessages(suppressWarnings(library(dplyr))) suppressMessages(suppressWarnings(library(tidyverse))) suppressMessages(suppressWarnings(library(scale...
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Data624_HW9
8.1. Recreate the simulated data from Exercise 7.2: library(mlbench) set.seed(200) simulated <- mlbench.friedman1(200, sd = 1) simulated <- cbind(simulated$x, simulated$y) simulated <- as.data.frame(simulated) colnames(simulated)[ncol(simulated)] <- "y" (a) Fit a random forest model to all of the predictors, then estimate the variable impor...
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Data624_HW7
6.2. 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: library(dplyr) library(varImp) library(elasticnet) a.Start R and use these commands to load the data: libr...
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