Publications by Daniel Moscoe

Predicting House Prices

library(GGally) library(MASS) library(modelr) library(tidyverse) library(stats) set.seed(235711) Introduction This data set contains 79 variables that describe houses in Ames, IA. The goal is to build a model that uses this data to explain the Sale Price for each house. Import Data model.dat <- read_csv("https://raw.githubusercontent.com/d...

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DATA 621 blog 5

This blog post is adapted from an assignment I completed for DATA 609. Newton’s Method and Gradient Descent Ordinary least squares is a special case of machine learning algorithms because model parameters can be computed by explicit formulas. In general, machine learning algorithms seek to minimize a cost/risk/loss function for which optima ca...

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DATA 621 blog 4

Principal Components Analysis Principal components analysis is a technique for reducing the dimensionality of a dataset. Reducing dimensionality would be easy if we didn’t have to worry about the explanatory power of our model– just drop variables! But how can you reduce dimensionality while preserving within the remaining explanatory variabl...

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