q1* files in this directoy demonstrate a linear regression model with the goal of predicting the median value of housing in an area based on 13 features. The fit of the model is judged using average squared error.
q2* files in this directory demonstrate a logistic regression classifier with regularization. The goal of the classifier is to classify a USPS handwritted digit dataset as either a 4 (class 0) or a 9 (class 1).
The project in this directory classifies movie reviews as positive or negative using the bag of words representation and a Multinomial Naive Bayes classifier with Laplace Smoothing. The project explores the impact of parameters such as alpha (q4), count vecotrizer parameters (q5), and the use of a validation set in addition to the testing and training sets.
This directory contains project code for a decision tree ensemble to predict election results from US county statistics. It contains code that generates a basic Decision Tree Classifier, in addition to a Random Forest generator, and an implementation AdaBoost.
This project explores k-means clustering and dimension reduciton (PCA) algorithms for identifying what activity (standing, running, sitting, etc...) a Sumsung Galaxy S3 phone detects its user is participating in based off of its accelerometer and gyros data.
Code authored by me and Aiden Nelson unless otherwise attributed.