In collaboration with my classmate, in our Statistical Machine Learning Course, we decided to look into Depression. Specifically, our questions were :
Given one’s marital status, gender, age, general health, physical health, slept hours, mental health, and having depression or not, how much would one make in terms of household income?
Given one's marital status, gender, age, general health, physical health, hours slept, mental health, and household income, do we predict that an individual is classified as being Depressed or not?
We used different methods to answer our questions, from decision trees, OLS models, GAMs with splines, and more!!
Our conclusions ended up concluding that there is various ins and outs of what other factors might partake in depression. Though, we have to consider that our models are not cause and effect model. Thus not everyone fits in such binary categories. Furthermore, we concluded that regression models weren’t the best for our data set.
Collaborators Michelle Dong and Alicia Severiano Perez
Link To view our presentation click the link below:
