Dataset Creation from Job Portals using Scrapping and EDA Implementation to Enhance Employability

dc.contributor.authorShah Priyanka
dc.contributor.authorVirpariya Paresh
dc.contributor.authorPandit Hardik
dc.date.accessioned2026-06-19T04:57:22Z
dc.date.issued2025-07
dc.description.abstractThe Computer Science is a highly sought-after field, offering some of the best job opportunities in the Information Technology sector through various job portals. Students often find that their skills in computing can open many doors in the rapidly evolving tech industry. There's a significant gap between the skills taught in educational settings and those demanded by the industry, largely due to the rapid evolution of technology. To address this, educational institutions need to frequently update their curricula collaborate with industry leaders to ensure students acquire relevant, up-to-date skills for the workforce. Thus, it's essential to analyze the skills that are currently in demand within the IT industry. A job portal is an online platform where companies can post job listings, offering a quick, reliable, and precise way to connect with potential employees. In this Research Paper, the Web Scrapping procedures and methods have been developed in Python to scrape employment details of Information Technology field from renowned job portals like Naukri.com, Monster.com, TimesJobs.com, Internshala.com and Myamcat.com. After scrapping, EDA (Exploratory Data Analysis) has been performed on scrapped data to summarize main characteristics of data set.
dc.identifier.issn2278-0181
dc.identifier.urihttp://160.160.1.15:4000/handle/123456789/433
dc.language.isoen
dc.publisherInternational Journal of Engineering Research & Technology (IJERT)
dc.relation.ispartofseriesVolume-14; No-7
dc.subjectcomponent Web Content Mining
dc.subjectWeb Scraping
dc.subjectExploratory Data Analysis
dc.subjectData Preprocessing
dc.subjectEmployability
dc.subjectKey skills
dc.titleDataset Creation from Job Portals using Scrapping and EDA Implementation to Enhance Employability
dc.typeArticle

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