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Data Science Salary Predictor: Project Overview

  • Created a tool that estimates data science salaries (MAE ~ $ 11K) to help data scientists negotiate their income when they get a job.
  • Scraped over 1000 job descriptions from glassdoor using python and selenium
  • 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

Python Version: 3.8.8
Packages: pandas, numpy, sklearn, matplotlib, seaborn, selenium, flask, json, pickle
For Web Framework Requirements: pip install -r requirements.txt
Scraper Github: https://github.com/arapfaik/scraping-glassdoor-selenium
Scraper Article: https://towardsdatascience.com/selenium-tutorial-scraping-glassdoor-com-in-10-minutes-3d0915c6d905
Flask Productionization: https://towardsdatascience.com/productionize-a-machine-learning-model-with-flask-and-heroku-8201260503d2

Web Scraping

Tweaked the web scraper github repo (above) to scrape 1000 job postings from glassdoor.com. With each job, we got the following:

  • 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 scraping the data, I needed to clean it up so that it was usable for our model. I made the following changes and created the following variables:

  • 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

I looked at the distributions of the data and the value counts for the various categorical variables. Below are a few highlights from the pivot tables.

alt text alt text alt text

Model Building

First, I transformed the categorical variables into dummy variables. 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 – Because of the sparse data from the many categorical variables, I thought a normalized regression like lasso would be effective.
  • Random Forest – Again, with the sparsity associated with the data, I thought that this would be a good fit.

Model performance

The Random Forest model far outperformed the other approaches on the test and validation sets.

  • Random Forest : MAE = 11.22
  • Linear Regression: MAE = 18.86
  • Ridge Regression: MAE = 19.67

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.