• Created a tool that estimates data science salaries (MAE ~ $12k) to help data scientists negotiate their income when they get a job

• Scraped over 1000 job descriptions 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 RidsearchCV to reach the best model.

• Built a client facing API using flask.

• Python: 3.7.6
• Packages: pandas, numpy, sklearn, matplotlib, seaborn, selenium, flask, json, pickle
• For Web Framework Requirements: pip install -r requirements.txt
• Guided DS Salaries Project: https://www.youtube.com/playlist?list=PL2zq7klxX5ASFejJj80ob9ZAnBHdz5O1t
• Scraper Git Hub: https://github.comarapfaik/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

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

• Job title
• Salary Estimate
• Job Description
• Rating
• Company
• Location
• Company Founded Date
• Type of Ownership
• Industry
• Sector
• Revenue

After scraping the data, I needed to clean it up so that it was usable for my 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
• 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
• Column for minimum degree reqirement

I looked at the distributions of the data and the value counts for the various categorical variables. Here are some data visualization highlights.

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.