This project aims to utilize various types of regression to forecast future values relating to Starbuck's yearly revenue, New York Times' quarterly revenue, Whole Foods quarterly revenue, and LinkedIn quarterly memberships in a time series analysis. The data and the general workflow of the code was provided by the Eastern University course "Data Science for Business".

This project documents the utilization of hierarchical and K-means clustering to create segmentations within subscribers based on varying attributes. These generated clusters could be used to aid this fictional magazine company in targeting specific subscriber demographics.

This project aim was to build and deploy a model that can predict whether or not a patient will have a stroke based on a variety of factors relating to patient data. This encompasses the patient’s medical history, and demographic information. The deployment of such a model could result in successful preventive care by focusing medical attention on high risk patients.