Related papers: From Code to Career: Assessing Competitive Programmers for Industry Placement

From Code to Career: Assessing Competitive Programmers for Industry Placement

URL: http://arxiv.org/abs/2508.00772v1
Date: Fri, 01 Aug 2025 16:52:44 GMT
Title: From Code to Career: Assessing Competitive Programmers for Industry Placement
Authors: Md Imranur Rahman Akib, Fathima Binthe Muhammed, Umit Saha, Md Fazlul Karim Patwary, Mehrin Anannya, Md Alomgeer Hussein, Md Biplob Hosen,
Abstract summary: This study focuses on predicting the potential of Codeforces users to secure various levels of software engineering jobs.<n>We collect user data using the Codeforces API, process key performance metrics, and build a prediction model using a Random Forest.<n>The model categorizes users into four levels of employability, ranging from those needing further development to those ready for top-tier tech jobs.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: In today's fast-paced tech industry, there is a growing need for tools that evaluate a programmer's job readiness based on their coding performance. This study focuses on predicting the potential of Codeforces users to secure various levels of software engineering jobs. The primary objective is to analyze how a user's competitive programming activity correlates with their chances of obtaining positions, ranging from entry-level roles to jobs at major tech companies. We collect user data using the Codeforces API, process key performance metrics, and build a prediction model using a Random Forest classifier. The model categorizes users into four levels of employability, ranging from those needing further development to those ready for top-tier tech jobs. The system is implemented using Flask and deployed on Render for real-time predictions. Our evaluation demonstrates that the approach effectively distinguishes between different skill levels based on coding proficiency and participation. This work lays a foundation for the use of machine learning in career assessment and could be extended to predict job readiness in broader technical fields.

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