[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123824-en":3,"doc-seo-123824-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123824,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Predicting and Recommending of Student Career Aspirations Using Machine Learning Models - Thesis Abstract","This thesis investigates using machine learning to predict and recommend students’ career aspirations from academic performance and extracurricular activities. Models including Logistic Regression, Random Forest, SVM, XGBoost, and a Neural Network approach are trained on a Kaggle dataset with 2000 students, using academic scores, personal attributes, and extracurricular activity features. The study identifies likely career paths to support educators with personalized, data-driven guidance. The Random Forest Classifier achieves the best performance and motivates an effective recommendation system.","UCLA  \nUCLA Electronic Theses and Dissertations  \nTitle  \nPredicting and Recommending of Student Career Aspirations Using Machine Learning Models  \nPermalink  \n[https://escholarship.org/uc/item/8nd7r80g](https://escholarship.org/uc/item/8nd7r80g)  \nAuthor  \nZhang, Lefan  \nPublication Date  \n2024  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA Los Angeles  \nPredicting and Recommending of Student Career Aspirations Using Machine Learning Models  \nA thesis submitted in partial satisfaction of the requirements for the degree Master of Applied Statistics and Data Science  \nby  \nLefan Zhang  \n2024  \n© Copyright by Lefan Zhang 2024  \nABSTRACT OF THE THESIS  \nPredicting and Recommending of  \nStudent Career Aspirations  \nUsing Machine Learning Models  \nby  \nLefan Zhang  \nMaster of Applied Statistics and Data Science  \nUniversity of California, Los Angeles, 2024  \nProfessor Yingnian Wu, Chair  \nThis 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.  \nThe thesis of Lefan Zhang is approved.  \nAkram M. Almohalwas  \nNicolas Christou  \nGeorge Michailidis Yingnian Wu, Committee Chair  \nUniversity of California, Los Angeles  \n2024  \nTABLE OF CONTENTS  \n1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1  \n2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3  \n2.1 Dataset Introduction .................................. 3  \n2.1.1 Predictors ................................... 3  \n2.1.2 Response Variable .............................. 4  \n2.2 Data Cleaning ..................................... 4  \n2.3 EDA .......................................... 4  \n2.3.1 Career Aspirations .............................. 5  \n2.3.2 Gender .................................... 6  \n2.3.3 Weekly Self-Study Hours .......................... 7  \n2.3.4 Academic Scores ............................... 8  \n3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10  \n3.1 Data Manipulation .................................. 10  \n3.1.1 Encoding ................................... 10  \n3.1.2 SMOTE .................................... 10  \n3.1.3 Scaling .................................... 11  \n3.2 Multinomial Logistic Regression ........................... 12  \n3.2.1 Introduction of Multinomial Logistic Regression .............. 12  \n3.2.2 Result ..................................... 12  \n3.3 Random Forest .................................... 15  \n3.3.1 Introduction of Random Forest ........................ 15  \n3.3.2 Result ..................................... 16  \n3.4 SVM .......................................... 18  \n3.4.1 Introduction of SVM ............................. 18  \n3.4.2 Result ..................................... 19  \n3.5 XGBoost ....................................... 21  \n3.5.1 Introduction of XGBoost ........................... 21  \n3.5.2 Result ..................................... 22  \n3.6 Multilayer Perceptron ................................. 24  \n3.6.1 Introduction of Multilayer Perceptron .................... 24  \n3.6.2 Result ..............","cbCaivrTYA5XEes8","https://ap.wps.com/l/cbCaivrTYA5XEes8","pdf",864033,1,43,"English","en",105,"# Introduction\n# Data\n## Dataset Introduction\n## Data Cleaning\n## EDA\n# Methodology\n## Data Manipulation\n## Multinomial Logistic Regression\n## Random Forest\n## SVM\n## XGBoost\n## Multilayer Perceptron\n# Conclusion\n## Recommendation\n## Summary and Future Analysis","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"It addresses predicting and recommending students’ career aspirations using machine learning based on academic and extracurricular information.\"},{\"question\":\"Which dataset and student information are used?\",\"answer\":\"The work uses a Kaggle dataset covering 2000 students, including academic scores, personal information, and extracurricular activities.\"},{\"question\":\"Which model performs best for the recommendation task?\",\"answer\":\"The Random Forest Classifier demonstrates the highest performance and is used to develop the career aspiration recommendation system.\"}]","Predicting and Recommending of Student Career Aspirations Using Machine Learning Models - 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