Career Recommender System

Publication Date

Spring 2025

Presentation Length

Poster/Gallery presentation

College

College of Sciences & Mathematics

Department

Math and Computer Science, Department of

Student Level

Undergraduate

SPARK Category

Research

Faculty Advisor

Christina Davis

WELL Core Type

Occupational Wellness

SPARK Session

Data Science

Presentation Type

Poster

Summary

Traditional career guidance often relies on generalized advice, human counselors, or further strategies that offer limited personalization. In contrast, employing machine learning-based career recommender systems utilizes data to connect individuals with suitable career paths based on their interests, skills, and industry trends.

This project develops a Career Recommender System using the Field of Study vs. Occupation dataset from Kaggle to analyze key factors such as job satisfaction, industry growth rate, and work-life balance. By integrating methodologies such as natural language processing (NLP), K-Means clustering, and feature engineering, the system filters careers and users to deliver personalized recommendations. The objective of the Career Recommender System is to aid individuals in taking a step towards making more informed career decisions.

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