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Group-Project, Data Science Immersive at General Assembly

Project 5: Predicting employee total compensation

Authors:

  • Annie Wang
  • Hans Baumberger
  • Mason Lee
  • Sileshi Hirpa

Content of the Project:

  • Problem statement

  • Data cleaning and EDA

  • Data Preprocessing and Modeling

  • Model Selection

  • Next Steps

Problem statement:

Predict the total compensation for a prospective employee based on the professional background, companies and their locations, and macroeconomic factors.

  • Goals
    1. Provide a reasonable expectation for compensation negotiation
    2. Provide an important benchmark to the companies for competitive compensation package in recruitment.

The technical goal for our project is to maximize R^2 and minimize MSE.

  • Data cleaning and EDA

Our project is based on the datasets obtained from:

  1. Web scraping the Levels.fyi (which lets users compare career levels and compensation packages across different companies) with permission from the administration.
  2. Inflation rate from rateinflation.com
  3. Unemployment data from Data World.

The three datasets were merged after data cleaning and EDA.

Data Dictionary

Term Description
timestamp timestamp of compensation record submission
company company names
title employee's job title
totalyearlycompensation total compensation that an employee gets annually
location cities where the companies are located
yearsofexperience years of experience in a career
yearsatcompany experience years of an employee at a particular company
year year of timestamp
month month of timestamp
year_month year month of the data
inflation_rate the percentage at which a currency is devalued during a period
inflation_rate_3mos inflation rate of 3 months prior to record timestamp
state states in the US
employment_rate The percentage of the labor force that is employed
employment_rate_3mos employment rate of 3 months prior to record timestamp
  • Some of the EDA we used include:

  1. Features' correlation
    Correlation of features

  2. Top 10 total compensations by title

    Total copensation

  3. Workers' location (top 10)

    Total copensation

  4. Nationwide Inflation Rate

    Total copensation

  5. Nationwide Unemployment Rate

Total copensation

  • Model Performance

Most of our models took longer than anticipated amount of time during the hyperparameter tuning process and we decided to run a model (RandomForestRegression) on the AWS platform. The following table summarizes the models we evaluated and the best model the team agreed upon for the compensation preidction: Gradient Boosting Regressor (with GridSearch).

Model Training Score (R^2) Testing Score(R^2) MSE(Train) MSE(Test) Comment
Linear Regression(with no penality) 0.5193 -7.2931Xe^28 8286.35 1.22Xe^27
Lasso Regularization (CV) 0.5182 0.5143 8305.30 8157.45
Ridge Regularization (CV) 0.52 0.5097 8274.20 8234.28
Elastic Net Regularization (CV) 0.4483 0.4499 9511.19 9238.88
Random Forest Regression (with Gridsearch) 0.466 0.410 9060 10319
KNN Regressor (with Gridsearch) 0.9907 0.4762 158.35 9172.55
Gradient Boosting Regressor (no gridsearch) 0.5973 0.5318 6834.12 8198.40
Gradient Boosting Regressor (with gridsearch) 0.7131 0.5477 4867.52 7919.98 Best Model
Support Vector Regression (SVR) (without gridsearch) 0.1368 -0.1287
Support Vector Regression (SVR) (with Gridsearch) 0.5029 0.4745
AdaBoost (with Gridsearch) 0.1930 0.1276 13693 15276

Next Steps

  • Incorporate more personal background features of the employee into analysis (i.e. Education)
  • Incorporate more company and industry background information (i.e. Stock price, Company size, Industry sector)
  • Include current data (after Sep. 2020)
  • More hyperparameter tuning (GridSearch, RandomizedSearch, BayesSearch)