[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128183-en":3,"doc-seo-128183-105":29,"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":21,"html_lang":23,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},128183,549768702563,"Sage","https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083",8,"Research & Report","Predicting Housing Tenure Decisions in Los Angeles Using Several Machine Learning Algorithms","The thesis investigates how multiple machine learning algorithms can predict housing tenure decisions in Los Angeles. It formalizes the problem, reviews relevant prior work, and describes a full data pipeline including extraction/collection, cleaning and integration, handling missing values, and feature engineering. The study evaluates several models—Random Forest, SVM, Naive Bayes, KNN, XGBoost, and GBM—through an experimental setup, using evaluation metrics to compare performance. Results are discussed with recommendations and directions for future research.","CALIFORNIA STATE UNIVERSITY, NORTHRIDGE  \nPredicting Housing Tenure Decisions in Los Angeles Using Several Machine Learning Algorithms  \nA thesis submitted in partial fulfillment of the requirements For the degree of Master of Science in Computer Science  \nBy  \nAigerim Toleukhanova  \nDecember 2024  \n© Copyright by Aigerim Toleukhanova 2024  \nii  \nThe thesis of Toleukhanova Aigerim is approved:  \nJeff Wiegley, Ph.D. Date  \nKyle Dewey, Ph.D. Date  \nJohn Noga, Ph.D., Chair Date  \nCalifornia State University, Northridge  \nDEDICATION  \nI dedicate this research paper to my family, whose encouragement and belief in me have been the foundation of my academic journey. Their love, support, and guidance have been a constant source of inspiration, motivating me to pursue my dreams and overcome challenges along the way. This work is a reflection of their sacrifices, patience, and faith in my potential. Tomy family, thank you for standing beside me every step of the way.  \nCalifornia State University, Northridge  \nTABLE OF CONTENT  \nCOPYRIGHT PAGE....................................................................................................................... ii  \nSIGNATURE PAGE....................................................................................................................... iii  \nDEDICATION................................................................................................................................ iv  \nLIST OF FIGURE..........................................................................................................................vii  \nABSTRACT..................................................................................................................................viii  \nCHAPTER 1: INTRODUCTION.................................................................................................... 1  \nProblem Description............................................................................................................ 1  \nInitial Studies....................................................................................................................... 2  \nCHAPTER 2: LITERATURE REVIEW......................................................................................... 4  \nRelated Work in the Domain................................................................................................4  \nAdaptation from Related Works.......................................................................................... 6  \nData Extraction/ Collection..................................................................................................8  \nData Preprocessing: Cleaning and Integration.....................................................................9  \nDealing with NAs and Feature Engineering...................................................................... 11  \nEmployment of AdaBoost For Feature Extraction............................................................ 13  \nCHAPTER 4: ALGORITHM AND MODELS............................................................................. 15  \nRandom Forest................................................................................................................... 15  \nSupport Vector Machines (SVM).......................................................................................16  \nNaive Bayes....................................................................................................................... 17  \nXGBoost............................................................................................................................ 17  \nGradient Boosting Machine (GBM).................................................................................. 18  \nKNN................................................................................................................................... 18  \nCHAPTER 5: EXPERIMENTAL SETUP.................................................................................","cbCaivRNE31YeU7f","https://ap.wps.com/l/cbCaivRNE31YeU7f","pdf",4991192,1,105,"English","en","# Chapter 1: Introduction\n## Problem Description\n## Initial Studies\n# Chapter 2: Literature Review\n## Related Work in the Domain\n## Adaptation from Related Works\n## Data Extraction/ Collection\n## Data Preprocessing: Cleaning and Integration\n## Dealing with NAs and Feature Engineering\n## Employment of AdaBoost For Feature Extraction\n# Chapter 4: Algorithm and Models\n## Random Forest\n## Support Vector Machines (SVM)\n## Naive Bayes\n## XGBoost\n## Gradient Boosting Machine (GBM)\n## KNN\n# Chapter 5: Experimental Setup\n# Chapter 7: Evaluation Metric\n# Chapter 8: Software and Tool\n# Chapter 9: Experimental Results and Discussion\n## Random forest\n## SVM\n## Naive Bayes\n## KNN\n## XGBoost\n## GBM\n# Chapter 10: Model Comparison\n## Discussion of Results\n# Chapter 11: Recommendations\n## Summary of Key Findings\n## Contributions of the Thesis\n## Recommendations for Future Research\n# Chapter 12: Conclusion","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"It addresses predicting housing tenure decisions in Los Angeles using several machine learning algorithms.\"},{\"question\":\"What models are evaluated in the study?\",\"answer\":\"The thesis evaluates Random Forest, Support Vector Machines (SVM), Naive Bayes, KNN, XGBoost, and Gradient Boosting Machine (GBM).\"},{\"question\":\"How is the data prepared before modeling?\",\"answer\":\"The workflow includes data extraction/collection, preprocessing with cleaning and integration, handling missing values (NAs), and performing feature engineering.\"}]","Predicting Housing Tenure Decisions in Los Angeles Using Several Machine 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