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Data Science Salary Estimator: Project Overview

  • Created a project that estimates data science salaries (MAE ~ $ 11K) to help data scientists negotiate their income when they get a job.
  • Engineered features from the text of each job description to quantify the value companies put on python, excel, aws, and spark.
  • Optimized Linear, Lasso, and Random Forest Regressors using GridsearchCV to reach the best model.
  • Built a client facing API using flask

Code and Resources Used

Data

  • In the data, with each job, we got the following attributes:
    • Job title
    • Salary Estimate
    • Job Description
    • Rating
    • Company
    • Location
    • Company Headquarters
    • Company Size
    • Company Founded Date
    • Type of Ownership
    • Industry
    • Sector
    • Revenue
    • Competitors

Data Cleaning

After getting the data, I needed to clean it up so that it can be used for our model. I made the following changes:

  • Parsed numeric data out of salary
  • Made columns for employer provided salary and hourly wages
  • Removed rows without salary
  • Parsed rating out of company text
  • Made a new column for company state
  • Added a column for if the job was at the company’s headquarters
  • Transformed founded date into age of company
  • Made columns for if different skills were listed in the job description:
    • Python
    • R
    • Excel
    • AWS
    • Spark
  • Column for simplified job title and Seniority
  • Column for description length

EDA

When I was visualizing the data and the value counts for the various categorical variables found some correlation and built some table tables for building a better model. Here are some of the pictures.

Model Building

First, i needed to create dummy variables as the data contains lots of catagorical attributes. I also split the data into train and tests sets with a test size of 20%.

I tried three different models and evaluated them using Mean Absolute Error. I chose MAE because it is relatively easy to interpret and outliers aren’t particularly bad in for this type of model.

I tried three different models:

  • Multiple Linear Regression – Baseline for the model
  • Lasso Regression
  • Random Forest

Model performance

The Random Forest model performed the best to the other approaches on the test and validation sets.

  • Random Forest : MAE = 10.89
  • Linear Regression: MAE = 18.56
  • Lasso Regression: MAE = 19.65

Productionization

In this step, I built a flask API endpoint that was hosted on a local webserver by following along with the TDS tutorial in the reference section above. The API endpoint takes in a request with a list of values from a job listing and returns an estimated salary.