Publications by Don Padmaperuma
DATA 622 Homework 2
Homework 2 - LDA / QDA / Naive Bayes Analysis We will be working with the Penguin dataset again as we did for Homework #1. Please use “Species” as your target variable. For this assignment, you may want to drop/ignore the variable “year”.We will be conducting Linear Discriminant Analysis, Quadratic Discriminant Analysis and Naive Bayes fo...
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Data 622 - Homework 3
Authorship Group 5: Don (Geeth) Padmaperuma, Subhalaxmi Rout, Isabel Ramesar, and Magnus Skonberg Background The purpose of this assignment was to explore classification via K-nearest neighbors, Decision Trees, Random Forests, and Gradient Boosting. Classification Classification is a supervised machine learning technique whose main purpose is ...
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DATA 622 - Final Project
Authorship Group 5: Don (Geeth) Padmaperuma, Subhalaxmi Rout, Isabel Ramesar, and Magnus Skonberg Background The purpose of our Final Project was to explore the application of Neural Networks to loan approval data to then back compare model performance with a variety of Classification algorithms (ie. KNN, DT, RF, GBM). Neural Networks Neural n...
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Data 622 - Homework 4 Question 2
Authorship Group 5: Don (Geeth) Padmaperuma, Subhalaxmi Rout, Isabel R., and Magnus Skonberg Background The purpose of this assignment was to explore Clustering, Principal Component Analysis, and Support Vector Machines. Clustering is used as a means of grouping a set of observations in a way that those in the same group are more similar to one...
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DATA 609 Homework 8
Ex.1 use the nnet package to analyze the iris dataset. Use 80% of the 150 samples as the training and the rest for validation. Discuss the result. library(nnet) library(ggplot2) ## Warning: package 'ggplot2' was built under R version 4.1.2 library(caret) ## Warning: package 'caret' was built under R version 4.1.2 ## Loading required package: lat...
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DATA 609 Homework 6
Ex.1 Use a dataset such as the PlantGrowth in R to calculate three different distance metrics and discuss the results. PlantGrowth data set which pertains to Results from an Experiment on Plant Growth. Euclidean Distance This represents the shortest distance between two points. PlantGrowth_euclidean <- dist(PlantGrowth, method = "euclidean") ## ...
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DATA 609 Homework 5
Ex.1 Carry out the logistic regression (Example 22 on page 94) in R using the data. x <- c(0.1, 0.5, 1.0, 1.5, 2.0, 2.5) y <- c(0, 0, 1, 1, 1, 0) The formula is \[y(x)=\frac{1}{1+exp[-(a+bx)]}\] glm(y ~ x, family = "binomial") ## ## Call: glm(formula = y ~ x, family = "binomial") ## ## Coefficients: ## (Intercept) x ## -...
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DATA 609 Homework 4
Ex.1 For example 19 on page 79 in the book, carry out the regression using R. x=c(-0.98, 1.00, 2.02, 3.03, 4.00) y=c(2.44,-1.51, -0.47, 2.54, 7.52) model1 <- lm(y~x) model1 ## ## Call: ## lm(formula = y ~ x) ## ## Coefficients: ## (Intercept) x ## 0.4038 0.9373 summary(model1) ## ## Call: ## lm(formula = y ~ x...
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DATA 609 Homework 3
Ex1 Write down Newton’s formula for finding the minimum of \(f(x) = (3x^4 - 4x^3)/12\) in the range of [-10,10]. Then, implement it in R. \(f(x) = (3x^4 - 4x^3)/12\) \(f'(x) = (12x^3 - 12x^2)/12 = x^3-x^2\) \(f''(x) = 3x^2-2x\) Let’s take a look at the curve of f(x) and f’(x) f <- function(x){ (3*x^4 - 4*x^3)/(12) } f_prime <- function...
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DATA 609 Homework 1
Don Padmaperuma Ex. 1 Find the minimum of \(f(x,y)=x^2+xy+y^2\)in\((x,y) \in R^2\) Two first partial derivatives \(\displaystyle \frac{\partial f}{\partial x}\) and \(\displaystyle \frac{\partial f}{\partial y}\) thus the stationary conditions means: \(\displaystyle \frac{\partial f}{\partial x} = 2x +y = 0\), \(\displaystyle \frac{\partial f}{\...
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