# Unlocking Futures: A Natural Language Driven Career Prediction System for Computer Science and Software Engineering Students

arXiv: https://arxiv.org/abs/2405.18139 · HTML: https://mlsanity.com/paper/2405.18139/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 25 references that are in the map.
- First posted on arXiv 28 May 2024.

## Builds on (most cited first, 2 of 2)

- [Learning Curves for Analysis of Deep Networks](https://mlsanity.com/paper/2010.11029/) (2020)
- [Accelerating Text Mining Using Domain-Specific Stop Word Lists](https://mlsanity.com/paper/2012.02294/) (2020)

## Abstract (arXiv)

A career is a crucial aspect for any person to fulfill their desires through hard work. During their studies, students cannot find the best career suggestions unless they receive meaningful guidance tailored to their skills. Therefore, we developed an AI-assisted model for early prediction to provide better career suggestions. Although the task is difficult, proper guidance can make it easier. Effective career guidance requires understanding a student's academic skills, interests, and skill-related activities. In this research, we collected essential information from Computer Science (CS) and Software Engineering (SWE) students to train a machine learning (ML) model that predicts career paths based on students' career-related information. To adequately train the models, we applied Natural Language Processing (NLP) techniques and completed dataset pre-processing. For comparative analysis, we utilized multiple classification ML algorithms and deep learning (DL) algorithms. This study contributes valuable insights to educational advising by providing specific career suggestions based on the unique features of CS and SWE students. Additionally, the research helps individual CS and SWE students find suitable jobs that match their skills, interests, and skill-related activities.