We attempted to apply a novel approach to stock market predictions. The Logistic Regression machine learning algorithm (Joseph Berkson) was applied to analyze news article headlines as represented by a bag-of-words (tri-gram and single-gram) representation in an attempt to predict the trends of stock prices based on the Dow Jones Industrial Average. The results showed that a tri-gram bag led to a 49% trend accuracy, a 1% increase when compared to the single-gram representation’s accuracy of 48%.

In the following paper, I aim to form relationships between different patient factors and no-show rates. The culmination of these relationships will then be used in a logistic regression model. Data collected from a survey at 26 HonorHealth clinics were analyzed using odds ratios and relative risk methods. Of 310,307 visits collected, 22,280 of them were no shows (7.2%), an 11% decrease from national averages (18.8%). This fueled the study, along with a grant filed by HonorHealth looking at the impact of telehealth on the working poor. A binary logistic regression method was run over the data, and less than 1% of patients' no-shows were predicted correctly. By adding factors, and improving the diversity in the data collected, model accuracy can be improved.