Smart Career Advisor Using Machine Learning

Abstract

Determining the ideal career path is a significant milestone in a student’s life. Unfortunately, students often face a lack of proper career counselling and awareness about options that align with their individual talents and preferences. This paper introduces a Smart Career Advisor built on machine learning principles to address these gaps. Leveraging models like Random Forest and XGBoost, the system analyses user-specific data such as academic records, interests, and skillsets to provide accurate, customized career suggestions for different education levels, including 10th, 12th, Diploma, Graduation, and Post-Graduation. The objective is to empower students with data-driven insights to make informed decisions about their careers.

Introduction

In today’s highly competitive environment, students often struggle to identify suitable career paths due to lack of personalized guidance. The Smart Career Advisor project offers a machine learning–based solution that provides tailored career recommendations using Random Forest and XGBoost algorithms. It takes into account individual academic performance, skills (technical, communication, logical reasoning), and personal interests to recommend suitable career options.

Key Features

• Personalized Career Suggestions: Based on user qualifications (10th, 12th, diploma, graduate, or postgraduate) and skill profiles.

• Skill Gap Analysis: Identifies gaps between current user skills and career requirements, offering improvement suggestions.

• User Profiling: Matches users with careers aligned to their educational level and strengths.

Related Work

• Prior models used Decision Trees, SVM, and Naive Bayes for career prediction.

• Existing systems often fall short in handling complex user profiles and dynamic career landscapes.

Proposed Enhancements

• Uses an ensemble approach combining Random Forest and XGBoost for greater accuracy and better pattern recognition.

• Offers a user-friendly web interface for input collection and recommendation delivery.

• Includes modules for data preprocessing, skill mapping, and job trend prediction.

Modules & Algorithms

1. User Interface Module – Collects user inputs and displays recommendations.

2. Data Preprocessing – Prepares education and skills data for training.

3. Recommendation Engine – Uses Random Forest and XGBoost to classify and suggest career paths.

4. Skill Matching Module – Compares existing vs. required skills.

5. Scrapy – Web scraping tool for real-time job data extraction.

Implementation & Results

• Built using supervised machine learning models trained on diverse career datasets.

• Delivers career suggestions alongside skill improvement recommendations.

• Includes visual dashboards and prediction outputs to enhance usability.

Conclusion

The Smart Career Advisor effectively combines Machine Learning (Random Forest, Xgboost) to provide accurate and personalized career recommendations. It helps students make informed decisions based on their interests, skills, and academic strengths. This system is a step toward smarter, data-driven career guidance in the digital age.

References

[1] J. Sharma and A. Shukla, “A Machine Learning Approach to Career Counselling,” 2018, Doi:10.1109/GUCON.2018.8675029. [2] A. Sharma, R. Verma, and M. Agarwal, “Career Counselling System Using Machine Learning”, Dec. 2017. [3] B. Bhargavi and P. S. Prasad, “An Efficient Career Counselling System Using Hybrid Model” ,Jan. 2018. [4] M. Kumar, S. R. Singh, and A. Gupta, “A Smart Career Guidance System Using Machine Learning,” May 2019. [5] A. Geron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd ed., O’Reilly Media, 2019.

Copyright

Copyright © 2025 Mrs. D. Urlamma, T. Naga Vasavi, N. Sucharitha, P. Supriya, Sk. Aaisha. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.