Publications by Devin Teran, Gabe Abreu, Amit Kapoor, Subhalaxmi Rout
Data 605 Assignment 15
Data 605 HW #15 Find the equation of the regression line for the given points. Round any final values to the nearest hundredth, if necessary. ( 5.6, 8.8 ), ( 6.3, 12.4 ), ( 7, 14.8 ), ( 7.7, 18.2 ), ( 8.4, 20.8 ) pt1 <- c(5.6, 6.3, 7, 7.7, 8.4) pt2 <- c(8.8, 12.4, 14.8, 18.2, 20.8) reg <- lm(pt2 ~ pt1) summary(reg) ## ## Call: ## lm(form...
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Data 608 HW1
Principles of Data Visualization and Introduction to ggplot2 I have provided you with data about the 5,000 fastest growing companies in the US, as compiled by Inc. magazine. lets read this in: library(psych) library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## Th...
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Data 621 Blog 1
library(MASS) Stepwise Regression One of the first concepts learned as a data scientist is regression, specifically Simple Linear Regression. As a data scientist continues on their and is exposed to a variety of data sets, they will quickly realize that Simple Linear Regression will either not suffice or be the optimal approach to solve a proble...
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Data 621 Blog 2
Data Exploration One of the initial steps in solving any data science problem is the process of data exploration. Data exploration entails finding missing variables, outliers, data distribution, and some visualizations to to get an overall big picture. We are going to use kaggle’s House Price competition data to perform data exploration using t...
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Data 621 HW#3
INTRODUCTION The aim of this assignment is to build a binary logistic regression model to predict whether a neighborhood will be at risk for high crime levels, using a data set containing information on crime for various neighborhoods of a major city. Each record has a response variable indicating whether or not the crime rate is above the median...
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Data 621 HW#1
Abstract To see how regression will help us evaluate baseball team performance, this project is designed to explore whether a teams success in any given season can be predicted or explained by any number of statistics in that season. Our goal is to build a multiple linear regression model on the training data to predict the number of wins for the...
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Data 621 HW#2
Objective Classification is the process of predicting a categorical label of a data object based on its features and properties. In this assignment we created R functions to calculate several different classification metrics as R functions. We also verified the functions by checking R package implementations against our output. Lastly, we graphed...
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Data 621 HW#4
Overview This assignment is about to explore, to analyze and to model a data set containing approximately 8000 records representing a customer at an auto insurance company. Each record has two response variables. The first response variable, TARGET_FLAG, is a 1 or a 0. A “1” means that the person was in a car crash. A zero means that the pers...
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Data 621 Blog 3
Data Imputation The last post discussed data exploration through data visualizations. In the exploratory phase, you may come across columns with a mixed degree of missing rows. Columns with high percentages of missing rows should be removed as you would be substituting too much of the data. Columns with small percentages could be either ignored o...
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Data 624 HW 9
Data 624 HW9 11/21/2021 Gabe Abreu 8.1 8.2 8.3 8.7 8.1 Recreate the simulated data from Exercise 7.2: 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" Fit a random forest model to all of the predictors, th...
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