Estimating Career Path using Supervised Machine Learning on Skills Assessment & Continuous Evaluation for Students | IEEE Conference Publication | IEEE Xplore

Estimating Career Path using Supervised Machine Learning on Skills Assessment & Continuous Evaluation for Students


Abstract:

The research paper focuses on revolutionizing student placement predictions in educational institutions through the integration of machine learning and data visualization...Show More

Abstract:

The research paper focuses on revolutionizing student placement predictions in educational institutions through the integration of machine learning and data visualization techniques. Acknowledging the pivotal role of placements in shaping a college's reputation, the study proposes a comprehensive approach to enhance student preparedness and optimize placement outcomes. The paper introduces a unified platform leveraging exploratory data analysis (EDA) and machine learning models, such as Linear Regression (LR), to provide real-time insights into students' placement readiness. Notably, the research addresses the shortcomings of existing systems by emphasizing the need for a consolidated platform that not only predicts placements but also guides students through their preparation journey.
Date of Conference: 05-07 April 2024
Date Added to IEEE Xplore: 10 June 2024
ISBN Information:
Conference Location: Pune, India

I. Introduction

Securing good placements is crucial for schools and colleges. A college's reputation hinges on successful student job placements. To achieve this, colleges employ a predictive system to assess students' suitability for specific jobs, facilitating early job placement and better preparation. A high placement rate enhances a college's reputation, aiding in admissions and improved teacher training. Manual management of student records is challenging, prompting the use of machine learning algorithms for efficient placement prediction. These algorithms, such as SVM, LR, Naive Bayes, and Decision Tree, filter students based on grades and skills, guiding future improvements for student placements.

References

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