shuchita-rahman / data-science-salary-prediction-project

Data-Science-Salary-Prediction-Project : Overview

• Collected 1000 Data Science job description of glassdoor which is scrapped using selenium.
• Engineered features from the text of each job description to quantify the value companies put on a bachelor's degree, python, excel, aws, statistics and spark.
• Optimized Linear, Lasso, and Random Forest Regressors using GridsearchCV to reach the best model.
• Created a tool that predicts data science salaries worldwide.

Data Cleaning

• Created new columns minimum, maximum and average salary for a specific job.
• Created new columns for employer-provided salary and hourly wages.
• Removed row without salary information.
• Simplified company name.
• Parsed jobs state and created a new column which consists of states' abbreviation name.
• Perceived age of a company from the company foundation date.
• Created columns for different skills, if required in the job description:
  • Python
  • R
  • Excel
  • AWS
  • Spark
  • Bachelors' Degree
  • Statistics
  • SQL
• Created a new column for description length.
• Removed unnecessary columns.

Exploratory Data Analysis

tried to analyze the distribution of qualitative and quantitative values. Also tried to find out the correlation between salaries and other variables.

Model Building

First, transformed the qualitative variables into dummies. Second, applied three different models:

• Multiple Linear Regression.
• Lasso Regression.
• Random Forest

Model Performance

• Random Forest : MAE - 18.69
• Lasso Regression : MAE 24.20
• Multiple Linear Regression: 122.02

Resources

Python Version: 3.7
Packages: pandas, numpy, sklearn, matplotlib, seaborn, pickle
Scraper Github: https://github.com/arapfaik/scraping-glassdoor-selenium
Article:

• https://towardsdatascience.com/selenium-tutorial-scraping-glassdoor-com-in-10-minutes-3d0915c6d905
• https://towardsdatascience.com/understanding-the-ols-method-for-simple-linear-regression-e0a4e8f692cc
• https://towardsdatascience.com/data-cleaning-with-python-and-pandas-detecting-missing-values-3e9c6ebcf78b

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