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A Novel Approach for Better Career Counselling Utilizing Machine Learning Techniques

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Abstract

The biggest issue many students face in today's world is choosing the right career. Especially when there are so many options available to them and the counselling options available are very limited or not very efficient, Career counselling is a very essential process that assists individuals in making informed decisions about their career paths. The use of machine learning in career counselling has gained so much attention due to its potential to analyse vast amounts of data and provide personalised guidance. Previously, there had been so much work done in this field with the help of artificial intelligence and machine learning, but there was a lack of a systematic system where students could explore each and every option thoroughly and get to know what the real outcome would be if they chose that stream. In this study, various factors such as the student’s interests, hobbies, past academics and performances, and achievements are taken into consideration to predict the right career option. The model is trained using five different machine learning algorithms: decision tree, Random Forest, Support Vector Machine, Nave Bayes, and K-nearest neighbours Classifier. Out of these, Random Forest gave the highest accuracy of 84.17%, and after hypertuning, it gave the highest accuracy of 85.68%. We also gave some manual inputs to the system and found out that the Random Forest gave the highest accuracy of 85.71%. The prediction results of each algorithm are summarised in this study.

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Data Availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Code Availability

Code for implementations are available on request due to privacy or other restrictions.

Abbreviations

AI:

Artificial intelligence

ANN:

Artificial neural networks

C3-IOC:

AI-based solution

CSS:

Cascading style sheets

ES:

Expert system

F1-Score:

Harmonic mean of precision and recall

FN:

False negative

FP:

False positive

GridSearchCV:

GridSearch cross-validation

HTML:

Hypertext markup language

ICT:

Information and communication technology

IT:

Information technology

KNN:

K-nearest neighbors algorithm

MySQL:

My structured query language

NB:

Naive Bayes

PHP:

Hypertext preprocessor

RBF:

Radial basis function

SKlearn:

Scikit-learn

SVM:

Support vector machine

TN:

True negative

TP:

True positive

XGBoost:

Extreme gradient boosting

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Funding

This study did not receive any funding in any form.

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Authors and Affiliations

  1. Medi-Caps University, Indore, India

    Kailash Chandra Bandhu, Ratnesh Litoriya, Mihir Khatri, Milind Kaul & Prakhar Soni

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  1. Kailash Chandra Bandhu
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  2. Ratnesh Litoriya
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Correspondence to Ratnesh Litoriya.

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Bandhu, K.C., Litoriya, R., Khatri, M. et al. A Novel Approach for Better Career Counselling Utilizing Machine Learning Techniques. Wireless Pers Commun 138, 2523–2560 (2024). https://doi.org/10.1007/s11277-024-11612-3

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