• 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

Python Version: 3.9

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

YouTube >> https://www.youtube.com/playlist?list=PL2zq7klxX5ASFejJj80ob9ZAnBHdz5O1t

Ken Jee's GitHub Profile >>> https://github.com/PlayingNumbers

❤️ ❤️ ❤️ And big Thank you to Ken Jee > this is my First end to end project 😊 😊 ❤️ ❤️ ❤️

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

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

• Python

• R

• Excel

• AWS

• Spark

• Column for simplified job title and Seniority

• Column for description length

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.

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.

• 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.

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

• Random Forest : MAE = 11.120102768456377

• Linear Regression: MAE = 3919437.2410207116 #

• Ridge Regression : MAE = 11.120102768456377

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.

https://github.com/tchapi/markdown-cheatsheet

guide >> https://stackoverflow.com/questions/14494747/how-to-add-images-to-readme-md-on-github

https://github.com/ikatyang/emoji-cheat-sheet/blob/master/README.md

https://www.webfx.com/tools/emoji-cheat-sheet/