Bayship and Yacht Company keeps track of data involved with their painting department. This data represents the time taken to complete several main processes. Currently, they use the average square footage rate from the data to predict how long the next task will take. Using models such as Simple and Multivariate Linear Regression allows Bayship to consider more features than just square footage and perform better on their time estimation.
Features that can be taken into consideration with multivariate linear regression include number of painters, material being used in process, different areas of the vessel being worked on. The results of our models showed fewer and smaller errors in time prediction. Bayship does not have a need for overtly complex models that require huge amounts of data; but instead could use practical simple versions of machine learning that give more insight rather than human experience. By implementing simple solutions, Bayship can quickly save time and money.