Mobility is one of the potential factors in contributing to the spread of COVID-19. Our goal is to estimate the effect of state-wide policies on mobility signals at a county-level.
The goal of this project is to construct a model for a given sentence and the label sentiment to predict what phrases in the sentence that best support the given sentiment.
This report is an analysis of STAR (Student-Teacher Achievement Ratio) project data specifically targeted the 1st graders’ dataset with teacher as the unit (class-level average score).
We aim to train a classifier to accurately classify a person whether or not has a heart disease problem. The dataset is provided by UCI Machine Learning Repository.