Salary Prediction Project (Python)
- Data Analysis and Visualization
- Model Building
- Linear Regression
- Polynomial Transformation
- Ridge Regression
- Random Forest
- Gradient Boosting
- Model Building
- 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.
| Variable | Description |
|---|---|
| jobId | Given JobID for the role |
| companyId | CompanyID for the respective jobId advertised |
| degree | Applicant's qualification |
| major | Degree specialization |
| industry | JobId's categorized industry such as Oil, Auto, Health, Finance, etc., |
| yearsExperience | Required Experience for the role |
| milesFromMetropolis | Distance of the job location in miles from the nearest metropolitan city |
| salary | In x1000 dollars of the respective jobId |
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 353 and r-squared of 0.764.
This model can provide the most accurate results when supplied with information on yearsExperience, milesFromMetropolis, jobType, degree and major.