Jesus Gastañaduy

Jesus Gastañaduy

M.S. in Business Analytics

Portfolio

Algorithms to predict Employee Attrition

It is well known that human capital is one of the main assets of any company, if not the most important one. For this reason, it is wise to lower the attrition rate as much as possible. To do this, we must first know who are the employees that are quitting and what are the variables which might be explaining this behavior. With this purpose in mind, we wanted to create an algorithm that could predict employee attrition. On this occasion, we worked with a fictional open dataset created by IBM data scientists.

Movies Recommendation System: GroupLens Dataset

Users usually consume a new product or service based on recommendations made by other users. This is clearly seen when deciding whether to watch or not to watch a movie. Companies such as Netflix use recommendation algorithms to predict how many stars a user will give a specific movie. Unfortunately, their data is not publicly available. However, the GroupLens research lab generated a dataset with over 10 million ratings for over 10,000 movies by more than 69,000 users. We used this dataset to create a movie recommendation algorithm