Salary Prediction Project (Python)

• Data Analysis and Visualization
• Linear Regression
• Polynomial Transformation
• Ridge Regression
• Random Forest

• Python 3
• Pandas
• NumPy
• Seaborn
• Scikit-learn
• Matplotlib
• SciPy
• Jupyter

The purpose of this project is to use data transformation and machine learning 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.

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

• Years Experience: How many years of experience
• Job Type: The position held (CEO, CFO, CTO, Vice President, Manager, Janitor, and senior or junior position)
• College Degree: Doctoral, Masters, Bachelors, High School, or None
• College Major: Biology, Business, Chemistry, Computer Science, Engineering, Literature, Math, Physics, or None
• Industry: Auto, Education, Finance, Health, Oil, Service, or Web
• Miles From Metropolis: How many miles away from a major city

Applying second order polynomial transformation to the features used gave the most accurate predictions with the least error when using a linear regression model. The result was a mean squared error of 354 with a 76% accuracy rate.

This model can be used as a guide when determining salaries since it shows reasonable predictions when given information on years of experience, miles from metropolis, job type, industry, and college degree and major.