DisYBoost: Physician Recommendation Predictions
Classification model built using XGBoost (ML/AI, Decision Trees, XGBoost, Python)
In this project, we are tasked with predicting whether or not physicians will will recommend a product from our company (Product X) to treat a particular disease (Disease Y). The datset provided encompasses patient Chart data that physicians have filled out for their patients. I ended up deciding to use an XGBoost based classifier for this task, and ultimately acheived an accuracy of ~85%, which we deemed a good model for this task given the data.
Below you can find some summary slides as well as the notebook including EDA and Model construction/testing. The full problem statement is given below.
You have been asked to participate in the evaluation of a novel new compound (Product X) being considered for the treatment of "Disease Y". The Medical Affairs team has partnered with leading physicians to complete a detailed chart review of thousands of potential patients. The physicians have documented other relevant treatments and diagnoses each patient has received. Disease Y presents in three types: mild, moderate, and severe. Using the data provided, please build a model to predict whether physicians are likely to recommend Product_X for any given patient. Summarize the data and your findings in a PowerPoint (or equivalent) deck of no more than 5 slides. Your target audience is a mixture of medical, marketing, and analytics leaders, with varying levels of data science familiarity.