This repository includes some of the projects I have worked on in my computational, data science, and machine learning courses. The code is my own work and the models were built by me using prompts from course assignments. The following are descriptions of the attached projects:
Patient Outcome Classification: Used data from the Framingham Heart Study to construct a logistic regression model to predict whether study participants experienced coronary heart disease within ten years of their first examination
Yelp Reviews: Predicted Yelp star ratings for a dataset of over 7000 restaurants by comparing logistic regression, classification and regression tree (CART) models Optical Letter Recognition: Compared prediction accuracy of logistic regression, CART, random forest, LDA, and boosting models for optical character recognition of a dataset of over 2000 images and bootstrapped confidence intervals for accuracy
Optical Letter Recognition: Compared prediction accuracy of logistic regression, CART, random forest, LDA, and boosting models for optical character recognition of a dataset of over 2000 images and bootstrapped confidence intervals for accuracy
Insurance Analytics: Constructed and analyzed a logisitic regression model and CART model to predict whether customers would be interested in purchasing vehicle insurance for the following year. As a part of this model, I conducted risk analysis and cost-minimization analysis.
Concrete Strength: I constructed a linear regression model to determine the strength of concrete based on a number of concrete-mixing components.