Publications by Jered Ataky
DATA 606 Project4
DATA 607 - Project 4 Jered Ataky, Magnus Skonberg 2020-11-14 Background The focus of this project is document classification. For this project, we will start with a corpus dataset, unzip our data, generate a training model that we’ll then use to predict the class of new documents (those withheld from the training set or taken from another sour...
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DATA606_Ass10
Librairies library (tidytext) library(tidyverse) ## -- Attaching packages --------------------------------------------------------------------------------------------------- tidyverse 1.3.0 -- ## v ggplot2 3.3.2 v purrr 0.3.4 ## v tibble 3.0.3 v dplyr 1.0.2 ## v tidyr 1.1.2 v stringr 1.4.0 ## v readr 1.3.1 v forcats 0...
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Data605_Ass4
Libraries Load needed libraries: library(doParallel) library(foreach) library(jpeg) library(kableExtra) library(OpenImageR) library(EBImage) Use of Graphics Put the images together on a single object files num=17 files=list.files("D:/Jered/Images/jpg",pattern="\\.jpg")[1:num] View Shoes Functions Read the file & get the resolution height...
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math1
PROBLEM SET 1 (1) The rank of matrix A \[\begin{gather*} I\ am\ going\ to\ use\ RREF\ to\ find\ the\ rank\ of\ A\\ \\ A\ =\begin{bmatrix} 1 & 2 & 3 & 4\\ -1 & 0 & 1 & 3\\ 0 & 1 & -2 & 1\\ 5 & 4 & -2 & -3 \end{bmatrix} R_{2}\rightarrow R_{3} \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ ...
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DATA605-Final_Project
Problem 1 Generating random numbers… # Set seed and define N, n N <- 6 n <- 10000 # Random uniform numbers X <- runif(n, 1, N) # Random normal numbers u <- (N + 1)/2 Y <- rnorm(n, mean=u, sd = u) Defining x and y… x <- quantile(X, 0.5) y <- quantile(Y, 0.25) We are going to use the following probabilities formulas: P( A/B) = ; ...
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DATA621-Post5
Overview To evaluate the performance of a logistic regression, we need some metrics which will help us to know better about how well the model performs. Among those metrics are the accuracy score which predicts the overall accuracy of the model, the precision, the receiver operating characteristic curve (ROC) which illustrates the diagnostic abil...
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DATA621-Post2
Introduction As mentioned in one the blogs (you can find it here), a good way of doing backward elimination is to use stepwise function function “step”. Here we are going to build a full multiple linear model, then apply step function to it to eliminate no needed variables. The general idea behind backward-selection is to start with the full ...
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DATA621-Post1
Introduction In this blog, we are going to do residual analysis.The residual analysis help us to fugue if a linear model is appropriate to a given data set. Prior to start, you will need to load both “tidyverse” and “openintro” where the dataset used is found. The data set is called evals, gathered from end of semester student evaluations...
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DATA621-Post3
Overview Here, we will demonstrate the power of mathematics transformation when performing linear regression. We are going to use a real-world data from 2008 collected by WHO, and can be accessed on my github repository here The variables included follow. Country: name of the country LifeExp: average life expectancy for the country in years Infan...
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DATA621-Post4
Introduction Prediction is one most the main goal of building a regression model. In one my blogs ( read here), I spoke about mathematics transformation for linear regression. Today, we are going to use the same data set and go over a transformed model to predict some values. Just to recall, it ’s a real-world data from 2008 collected by WHO, a...
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