國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Predicting and Recommending of Student Career Aspirations Using Machine Learning Models./
作者:	Zhang, Lefan.
面頁冊數:	1 online resource (43 pages)
附註:	Source: Masters Abstracts International, Volume: 85-12.
Contained By:	Masters Abstracts International85-12.
標題:	Computer science. -
電子資源:	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:

573171
Computer science.
Subjects--Index Terms:

PredictionsIndex Terms--Genre/Form:

554714
Electronic books.

Predicting and Recommending of Student Career Aspirations Using Machine Learning Models.
LDR:02190ntm a22003857 4500 001 1146377
005 20240812064422.5
006 m o d
007 cr bn ---uuuuu
008 250605s2024 xx obm 000 0 eng d
020 $a 9798382838533
035 $a (MiAaPQ)AAI31331857
035 $a AAI31331857
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Zhang, Lefan. $3 1471760
245 1 0 $a Predicting and Recommending of Student Career Aspirations Using Machine Learning Models.
264 0 $c 2024
300 $a 1 online resource (43 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 85-12.
500 $a Advisor: Wu, Yingnian.
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
650 4 $a Computer science. $3 573171
650 4 $a Statistics. $3 556824
653 $a Predictions
653 $a Recommendations
653 $a Student career aspirations
653 $a Machine learning models
655 7 $a Electronic books. $2 local $3 554714
690 $a 0463
690 $a 0984
690 $a 0800
710 2 $a University of California, Los Angeles. $b Applied Statistics And Data Science 00J3. $3 1471761
710 2 $a ProQuest Information and Learning Co. $3 1178819
773 0 $t Masters Abstracts International $g 85-12.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31331857 $z click for full text (PQDT)