A Comparative Study on Machine Learning Algorithms for Predicting the Placement Information of Under Graduate Students

Aravind, T.; Reddy, Bhimavarapu Sasidhar; Avinash, S.; Jeyakumar, G.

doi:10.1109/i-smac47947.2019.9032654

Cited by 24 publications

(4 citation statements)

References 7 publications

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“…Besides, Beaulac and Rosenthal [12] (2019) and Çakıt and Dağdeviren [13] (2022) dig into foreseeing scholastic achievement and majors utilizing AI, offering a more extensive viewpoint past forecast of understudy situation. Different examinations, for example, those by Aravind et al [14] All in all, the writing gives sign of developing interest in anticipating understudy position and scholastic achievement utilizing AI models as well as related instructive results. These examinations add to the headway of prescient demonstrating procedures and proposition important experiences for teachers, scientists, and policymakers trying to improve understudy results through information driven approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

Career Path Insights

Jadhav,

Pandey,

Zine

et al. 2024

Preprint

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“Career Path Insights” is a digital platform that helps students navigate their path to success through a blend of technology, user engagement, and educational tools. It provides a user-friendly website with various features to support students in making well-informed career choices. Some of its key features include user registration, data input forms for academic and skill details, prediction of eligibility using machine learning, personalized career suggestions, user dashboards, interactive data display, a resume creator, and additional features to improve the overall user experience.

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Section: Literature Reviewmentioning

confidence: 99%

Career Path Insights

Jadhav,

Pandey,

Zine

et al. 2024

Preprint

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“…Machine Learning (ML) telah menjadi populer saat ini untuk memecahkan berbagai masalah dan mendukung dalam banyak proses pengambilan keputusan (Aravind et al, 2019).…”

Section: Prinsip Dalam Algoritmaunclassified

Model Klasifikasi Machine Learning untuk Prediksi Ketepatan Penempatan Karir

Mahmud Nawawi,

Baitul Hikmah,

Mustopa

et al. 2024

saintekom

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The complexity of the job market requires individuals and organizations to understand the trends and needs of the world of work. One of the main challenges is the right career placement. That is becoming increasingly popular is the use of Machine Learning algorithms in the decision-making process. ML classification models such as Random Forest, Decision Tree, Naïve Bayes, KNN, and SVM have demonstrated their potential in uncovering hidden patterns from data, including a person's educational history, work experience and interests. In this research, the application of the ML classification model is aimed at predicting career placement. From the data sample used of 215, this research evaluates the effectiveness of various ML models in the context of career placement. As a result, the Random Forest Model is superior to other proposed models with an accuracy value of 87% and an AUC/ROC value of 0.93 which indicates a very good classification value. Meanwhile, the SVM model with Linear Kernel shows the lowest performance with an accuracy value of 67%. Apart from getting information on the best accuracy and AUC/ROC values, the results of this research found that the 'ssc_presentage' attribute (high school exam percentage) is an important factor in career placement decisions.

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“…One with simple data and second one is with additional features of students. Root mean square error considered for the study [4]. According to Chandrasekhar Kumbhar, Dr. S. S. Sridhar, they have worked on decision tree, Neural network and Support vector machine algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Review on Predictive Analysis of Placement of Students Using Machine Learning Algorithms

Agrawal¹,

Kadam²

2023

Advances in Computer Science Research

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Machine learning is an emerging trend which has proven to learn automatically from past data. Machine learning can be performed using various ways like supervised, unsupervised, reinforcement learning. Machine learning where system computer system learning from the data set available. Machine learning using supervised learning can be performed using either regression or classifier algorithms. In this research paper we have worked on machine learning algorithms which are SVM, Decision Tree, Logistic regression and Random Forest. Using Machine learning algorithms we can build mathematical model and make the predictions using dataset. To make prediction we can go for supervised learning where output will be in the form either yes or No, 0 or 1, in this case either student placed or not placed.

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A Comparative Study on Machine Learning Algorithms for Predicting the Placement Information of Under Graduate Students

Cited by 24 publications

References 7 publications

Career Path Insights

Career Path Insights

Model Klasifikasi Machine Learning untuk Prediksi Ketepatan Penempatan Karir

Review on Predictive Analysis of Placement of Students Using Machine Learning Algorithms

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