[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117080-en":3,"doc-seo-117080-105":30,"detail-sidebar-cat-0-en-105":92},{"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},117080,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Income Prediction Using Machine Learning Techniques - Thesis","This thesis investigates income prediction, focusing on whether individuals earn more than $50,000 per year using advanced machine learning methods and demographic predictor variables such as education, occupation, relationship, capital gain, and capital loss. Built on the UCI Adult Income dataset, the study preprocesses data to ensure quality, then evaluates multiple algorithms including Logistic Regression, k-Nearest Neighbors, Decision Trees, Random Forests, Support Vector Machines, and Neural Networks. Systematic hyper-parameter tuning refines model performance, with Random Forest delivering the strongest results across common metrics, including accuracy, AUC, F1, sensitivity, specificity, and RMSE.","UCLA  \nUCLA Electronic Theses and Dissertations  \nTitle  \nIncome Prediction Using Machine Learning Techniques  \nPermalink  \n[https://escholarship.org/uc/item/6d01c9v7](https://escholarship.org/uc/item/6d01c9v7)  \nAuthor  \nJo, Kahyun  \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  \nIncome Prediction Using Machine Learning Techniques  \nA thesis submitted in partial satisfaction of the requirements for the degree Master of Applied Statistics & Data Science  \nby  \nKahyun Jo  \n© Copyright by Kahyun Jo 2024  \nABSTRACT OF THE THESIS  \nIncome Prediction Using Machine Learning Techniques  \nby  \nKahyun Jo  \nMaster of Applied Statistics & Data Science  \nUniversity of California, Los Angeles, 2024  \nProfessor Frederic R. Paik Schoenberg, Chair  \nThis thesis presents a comprehensive study on predicting income levels, specifically predicting whether individuals earn more than $50,000 per year, with advanced machine learning techniques, using various demographic predictor variables such as capital gain, education level, relationship, occupation, and capital loss. The prediction of income levels is crucial for elucidating economic disparities and informing policy decisions. Utilizing the Adult Income dataset from the UCI Machine Learning Repository, which comprises demographic and socio-economic variables, the research entails a thorough evaluation of each model’s performance. The methodology involves a preprocessing stage to ensure data quality, followed by the application of various machine learning algorithms including, but not limited to, Logistic Regression, k-Nearest Neighbors, Decision Trees, Random Forests, Support Vector Machines, and Neural Networks. A significant focus is placed on systematic hyper-parameter tuning to fine-tune models, particularly with the complex structures of Neural Networks and Random Forests. The findings indicate that Random Forest models exhibit superior performance in income prediction tasks across most metrics, including accuracy, sensitivity, precision, specificity, F1 score, AUC, and RMSE. The Baseline Random Forest achieves the best accuracy (86.410%), specificity (88.600%), and RMSE (0.315), suggesting strong overall performance and well-calibrated probabilities. The Tuned Random Forest achieves the highest AUC (94.964%) and F1 score (82.057%), indicating strong overall performance and an effective balance between precision and recall.  \nThe thesis of Kahyun Jo is approved.  \nNicolas Christou  \nYingnian Wu  \nFrederic R. Paik Schoenberg, Committee Chair  \nUniversity of California, Los Angeles 2024  \nTo my mother . . . who—among so many other things—  \nopened my eyes to the vast world  \niv  \nTABLE OF CONTENTS  \n1 Introduction ...................................... 1  \n2 Data Preparation and EDA ............................ 3  \n2.1 Data Preparation ................................. 4  \n2.2 Exploratory Data Analysis (EDA) ........................ 5  \n3 Methodology ..................................... 10  \n3.1 Logistic Regression ................................ 11  \n3.2 Logistic Regression with Oversampling ..................... 13  \n3.3 L1 and L2 Regularization ............................ 15  \n3.4 Naive Bayes .................................... 18  \n3.5 KNN ........................................ 20  \n3.6 Decision Trees ................................... 22  \n3.6.1 Constructing a Baseline Decision Tree ................. 22  \n3.6.2 Optimizing with Tree Pruning ...................... 24  \n3.6.3 Enhancing Predictive Power through Tree Tuning ........... 26  \n3.7 Random Forest .................................. 29  \n3.7.1 Baseline Random Forest ......................... 29  \n3.7.2 Random Forest with Tuned Parameters ................. 30  \n3.8 Support Vector Machine ............................. 32  \n3.9 Neural Network ...........................","cbCaid7pzG6XhXIl","https://ap.wps.com/l/cbCaid7pzG6XhXIl","pdf",1134000,1,58,"English","en",105,"# Introduction\n## Data Preparation and EDA\n# Methodology\n## Logistic Regression\n## Logistic Regression with Oversampling\n## L1 and L2 Regularization\n## Naive Bayes\n## KNN\n## Decision Trees\n## Random Forest\n## Support Vector Machine\n## Neural Network\n# Conclusion","[{\"question\":\"What income prediction task does the thesis focus on?\",\"answer\":\"The thesis predicts whether individuals earn more than $50,000 per year based on demographic and socio-economic variables.\"},{\"question\":\"Which datasets and variables are used for model training?\",\"answer\":\"It uses the UCI Adult Income dataset and includes predictors such as education, occupation, relationship, capital gain, and capital loss.\"},{\"question\":\"Which machine learning models and tuning strategy are evaluated?\",\"answer\":\"The study evaluates Logistic Regression, kNN, Decision Trees, Random Forests, SVM, and Neural Networks, with systematic hyper-parameter tuning to refine performance.\"}]","Income Prediction Using Machine Learning Techniques - Thesis | PDF",1785673606,146,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"income-prediction-using-machine-learning-techniques-thesis","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/income-prediction-using-machine-learning-techniques-thesis/117080/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What income prediction task does the thesis focus on?","Question",{"text":76,"@type":77},"The thesis predicts whether individuals earn more than $50,000 per year based on demographic and socio-economic variables.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which datasets and variables are used for model training?",{"text":81,"@type":77},"It uses the UCI Adult Income dataset and includes predictors such as education, occupation, relationship, capital gain, and capital loss.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning models and tuning strategy are evaluated?",{"text":85,"@type":77},"The study evaluates Logistic Regression, kNN, Decision Trees, Random Forests, SVM, and Neural Networks, with systematic hyper-parameter tuning to refine performance.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]