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Predicting and Recommending of Student Career Aspirations Using Machine Learning Models.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Predicting and Recommending of Student Career Aspirations Using Machine Learning Models./
Author:	Zhang, Lefan.
Description:	1 online resource (43 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-12.
Contained By:	Masters Abstracts International85-12.
Subject:	Statistics. -
Online resource:	click for full text (PQDT)
ISBN:	9798382838533

Predicting and Recommending of Student Career Aspirations Using Machine Learning Models.
Zhang, Lefan.

Predicting and Recommending of Student Career Aspirations Using Machine Learning Models. - 1 online resource (43 pages)

Source: Masters Abstracts International, Volume: 85-12.

Thesis (M.S.)--University of California, Los Angeles, 2024.

Includes bibliographical references

This thesis investigates the application of machine learning models for predicting and recommending student career aspirations based on academic performance and extracurricular activities. Logistic Regression, Random Forest, SVM, XGBoost, and Neural Network models are employed to analyze a dataset from Kaggle, which includes academic scores, personal information, and extracurricular activities of 2000 students. The study aims to identify potential career paths for students and assist educators in providing personalized guidance. Among the models, the Random Forest Classifier demonstrated the highest performance, leading to the development of an effective career aspiration recommendation system. This system has significant implications for enhancing student career development programs by offering data-driven insights and support.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382838533Subjects--Topical Terms:

556824
Statistics.
Subjects--Index Terms:

PredictionsIndex Terms--Genre/Form:

554714
Electronic books.

Predicting and Recommending of Student Career Aspirations Using Machine Learning Models.
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502 $a Thesis (M.S.)--University of California, Los Angeles, 2024.
504 $a Includes bibliographical references
520 $a This thesis investigates the application of machine learning models for predicting and recommending student career aspirations based on academic performance and extracurricular activities. Logistic Regression, Random Forest, SVM, XGBoost, and Neural Network models are employed to analyze a dataset from Kaggle, which includes academic scores, personal information, and extracurricular activities of 2000 students. The study aims to identify potential career paths for students and assist educators in providing personalized guidance. Among the models, the Random Forest Classifier demonstrated the highest performance, leading to the development of an effective career aspiration recommendation system. This system has significant implications for enhancing student career development programs by offering data-driven insights and support.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
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650 4 $a Computer science. $3 573171
653 $a Predictions
653 $a Recommendations
653 $a Student career aspirations
653 $a Machine learning models
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690 $a 0984
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