Open AccessSalary Estimator using Data Science
Author(s) -
Winner Walecha and Dr. Bhoomi Gupta
Publication year - 2020
Publication title -
international journal of modern trends in science and technology
Language(s) - English
Resource type - Journals
ISSN - 2455-3778
DOI - 10.46501/ijmtst061259
Subject(s) - salary , estimator , revenue , lasso (programming language) , random forest , computer science , linear regression , regression analysis , the internet , econometrics , data mining , data science , statistics , business , machine learning , mathematics , world wide web , economics , accounting , market economy
This paper presents a salary prediction system using the job listings from an employment website, in thiscase Glassdoor.com. A data mining technique is used to generate a model which will scrape number of jobsfrom the employment website, clean it on the basis of number of factors including the rival companies,revenue and skill required thereby predicting the salary to be expected when applying for a data science job.Techniques like linear regression, lasso regression, random forest regressors are optimised usingGridsearchCV to reach the best model. The model can be further extended to build a flask API thus can bedeployed on the internet for public usage.