Utilizing Machine Learning for Predictive Analytics in Career Path Forecasting - Anjali Jindia - Sonal Chawla

Aug 5, 2026

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Despite strong educational institutions in India, student dropout remains high, often driven by choosing an unsuitable career path. The study addresses this issue by providing data-driven guidance that supports informed career decisions aligned with students’ preferences and interests. Supervised machine learning is used for career-path prediction, employing algorithms such as Decision Tree, Random Forest, SVM, kNN, Naïve Bayes, AdaBoost, and Logistic Regression, with literature review, model comparison, dataset evaluation, and result-based conclusions.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Abstract

Introduction

Motivation

Challenges faced by machine learning while predicting career path options

Utilizing Machine Learning for Predictive Analytics in Career Path Forecasting Anjali Jindia Department of Computer Science and Applications, Panjab University, Chandigarh, India. [e-mail: ajindia82@gmail.com](e-mail: ajindia82@gmail.com) Sonal Chawla Department of Computer Science and Applications, Panjab University, Chandigarh, India. e-mail: [sonal_chawla@yahoo.com](sonal_chawla@yahoo.com) Abstract—Despite the presence of excellent educational institutions in India, the country still faces a high student dropout rate. This can be attributed to various factors, primarily being the selection of an inappropriate career path. To tackle this concern, it is crucial to offer students appropriate guidance, so that they can make informed decisions about their future careers which are aligned with their preferences and interests. This will help in avoiding future complications like discontentment, poor performance, fear and stress, social neglect etc. Machine Learning (ML) paves the path for students by making predictions regarding the future career path selection, with the help of application of various algorithms like Decision Tree (DT)[1], Random Forest (RF)[2], Support Vector Machine (SVM)[3], k Nearest Neighbor (kNN)[4], Naïve Bayes (NB) [5], Adaboost [6] and Logistic Regression (LR) . Therefore, the objective of this paper is four folds. Initially, to carry out an in-depth literature review emphasizing the application of ML techniques in predictive analysis. Top of Form Secondly, the paper compares and contrasts the ML techniques most suitable and apt for students’ choosing their career option. Thirdly, the paper applies these ML techniques on a dataset and evaluates them against different parameters like accuracy, precision and recall. Finally, the paper concludes while analyzing the results. Keywords-Career Prediction; Supervised ML; ML Classifiers, SVM, Adaboost, RF, DT I. INTRODUCTION In the contemporary landscape of workforce dynamics and career development, the utilization of predictive analytics and machine learning for career path prediction has emerged as a transformative area of research and practice. The ability to forecast and anticipate future career trajectories is of paramount importance for individuals navigating the complexities of the job market and for organizations seeking to strategically manage their talent pool. By harnessing advanced data analytics techniques and machine learning algorithms, researchers and practitioners aim to enhance the accuracy and reliability of career path predictions, thereby empowering individuals to make informed decisions about their professional growth and assisting organizations in talent acquisition, retention, and succession planning. This research topic delves into the intersection of predictive analytics, machine learning, and career development, exploring the potential of data-driven approaches to revolutionize how career paths are envisioned, planned, and navigated in the era of digital transformation and rapid technological advancements. Through a comprehensive review of existing literature, methodologies, and case studies, this review paper aims to provide insights into the current state-ofthe-art in predictive analytics for career path prediction, identify key challenges and opportunities in this domain, and outline future directions for research and application in leveraging machine learning for enhancing career planning and progression strategies. This paper centers on the application of various supervised machine learning algorithms on a students’ dataset using the methodology shown in Fig.1 below, aiming to ascertain the most efficient algorithm with the highest accuracy and precision for predicting the career path. The subsequent sections are structured as follows: Section 2 provides an overview of the existing literature; Section 3 offers a comparison of diverse machine learning algorithms used for the prediction tasks; Section

FAQs

What problem does the paper address in career path prediction?

It targets high student dropout in India, attributed largely to selecting an inappropriate career path, leading to dissatisfaction and poor performance.

Which machine learning algorithms are used for predictive career path forecasting?

The paper uses Decision Tree, Random Forest, Support Vector Machine, k Nearest Neighbor, Naïve Bayes, AdaBoost, and Logistic Regression.

How are the algorithms evaluated in the study?

They are applied to a dataset and assessed using metrics including accuracy, precision, and recall.

Aug 5, 2026

Upload by Riley