The purpose of this project is to make accurate salary preditctions that are based on known salaries so the company is able to recruit and retain top talent. This model will serve as a guide for offering competetive compensation to existing and future employees while controlling payroll expenses.

Data transformation and machine learning will be used to create a model that will predict a salary when given years of experience, job type, college degree, college major, industry, and miles from a metropolis.

The data for this model is fairly simplified as it has very few missing pieces. The raw data consists of a training dataset with the features listed above and their corresponding salaries. Twenty percent of this training dataset was split into a test dataset with corresponding salaries so accuracy and error of the model can be determined.

There is also a testing dataset that does not have any salary information available and was used as a substitute for real-world data