Publications by Irene Jacob
Data606_Lab 9
Grading the professor Many college courses conclude by giving students the opportunity to evaluate the course and the instructor anonymously. However, the use of these student evaluations as an indicator of course quality and teaching effectiveness is often criticized because these measures may reflect the influence of non-teaching related charac...
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DATA605 Final Project
Problem 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 a mean of \(\mu=\sigma=(N+1)/2\) set.seed(10) N <- 25 #i am choosing N as 25 n <- 10000 X <- ...
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DATA 621 Final Project
set.seed(123) mat_df <- data.frame(matrix(ncol = 3, nrow = 0) ,stringsAsFactors = FALSE) por_df <- data.frame(matrix(ncol = 3, nrow = 0) ,stringsAsFactors = FALSE) 1. Data Exploration mat <- read.csv("https://raw.githubusercontent.com/irene908/DATA621/main/student-mat.csv") por <- read.csv("https://raw.githubusercontent.com/irene908/DATA621...
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DATA 621 Blog 5
Blog 5 - One-Hot Encoding Introduction Categorical data refers to variables that are made up of label values like different categories that sometimes have a natural ordering to them. Some machine learning algorithms can work directly with categorical data depending on implementation, such as a decision tree, but most require the variables to be ...
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DATA 621 Blog 4
Blog 4 Forward Selection In my previous blog post I discussed about Backward elimination. In this post I will be discussing about Forward Selection using the same train dataset from assignment 3. Forward selection typically begins with only an intercept. One tests the various variables that may be relevant, and the ‘best’ variable—where �...
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DATA 621 Blog 3
Blog 3 Backward elimination BACKWARD STEPWISE REGRESSION is a stepwise regression approach that begins with a full (saturated) model and at each step gradually eliminates variables from the regression model to find a reduced model that best explains the data. Also known as Backward Elimination regression. The stepwise approach is useful because ...
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DATA621 Assignment 1
Assignment 1 1. Data Exploration train <- read.csv("https://raw.githubusercontent.com/irene908/DATA621/main/moneyball-training-data.csv") %>%select(-INDEX) test <- read.csv("https://raw.githubusercontent.com/irene908/DATA621/main/moneyball-evaluation-data.csv") %>%select(-INDEX) dim(train) ## [1] 2276 16 summary(train) ## TARGET_WINS T...
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DATA 621 Blog 2
Blog 2 Poisson Regression Poisson regression is used to model count variables. Poisson regression is similar to regular multiple regression except that the dependent (Y) variable is an observed count that follows the Poisson distribution. Thus, the possible values of Y are the nonnegative integers: 0, 1, 2, 3, and so on. It is assumed that large...
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DATA 621 Blog 1
Blog 1 Model Comparisons This blog describes a few model comparison packages using an example. Data blog1 <- read.csv("https://raw.githubusercontent.com/irene908/DATA621/main/Blog1.csv") Creating a copy of the data to store the log of PrizeMoney Logblog1 <- blog1 Logblog1$logPrizeMoney <- log(blog1$PrizeMoney) Logblog1$PrizeMoney <- NULL St...
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DATA608_Homework1
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: inc <- read.csv("https://raw.githubusercontent.com/charleyferrari/CUNY_DATA_608/master/module1/Data/inc5000_data.csv", header= TRUE) And lets preview this...
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