[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123356-en":3,"doc-seo-123356-105":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":4,"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},123356,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning and classification of transport mode choice using Python","The thesis investigates transport mode choice through a machine learning classification pipeline built with Python. It reviews key behavioral and contextual factors affecting travelers’ decisions, then covers core machine learning concepts, classification algorithms, and the impact of class imbalance. The work defines methodology, data collection steps, and a structured analysis procedure, followed by data preparation, exploratory analysis, and model training. Results are presented for two cases, including decision trees, random forests, XGBoost, stacked models, metrics, comparison, feature reduction, and model selection.","Master Thesis  \nMachine Learning and classification of transport mode  \nchoice using Python  \nMarios Melachroinos  \nSupervisor Thanasis Argyriou  \nMaster Thesis  \nMachine Learning and classification of transport mode  \nchoice using Python  \nMarios Melachroinos  \nSupervisor Thanasis Argyriou  \nCopyright ©Melachroinos Marios, 2024  \nAll rights reserved. Με επιφύλαξη παντός δικαιώματος .  \nThe approval of this thesis by the Department of Economics (MSc Administration, Analytics and Information Systems) of the National and Kapodistrian University of Athens does not necessarily imply the acceptance of the author's views on behalf of the Department.  \nI hereby certify the submitted thesis and the work presented is personal and that all sources and materials used have been properly referenced in the text and bibliography.  \nMarios Melachroinos  \nAcknowledgements  \nI would like to express my gratitude to my family for their continuous assistance throughout this journey. Their belief in my capabilities and constant motivation provided the strength needed to overcome the challenges of completing this master's thesis.  \nI extend my appreciation to Professor Thanasis Argyriou for his guidance and mentoring during this research journey. His expertise, constructive feedback, and dedication to academic excellence have significantly influenced the quality of this thesis.  \nAdditionally, I express my gratitude to Ioannis Demetriou, the head professor and the first person I encountered during the initial courses. His warm welcome, expertise, and guidance during the later stages of the Master's Program were invaluable.  \nFinally, my heartfelt thanks also go to my friends and colleagues who generously provided their perspectives and assistance at various stages of this research. Their collaboration added depth to the project and enriched the overall learning experience.  \nThis thesis is dedicated to all those who supported and inspired me. Their contributions have played a pivotal role in the completion of this academic pursuit.  \nCONTENTS  \nTABLE OF FIGURES ............................................................................................................... 8  \nABSTRACT............................................................................................................................. 16  \nΠΕΡΙΛΗΨΗ ............................................................................................................................. 17  \n1.INTRODUCTION ................................................................................................................ 11  \n2. LITERATURE REVIEW .................................................................................................... 13  \n2.1. Factors influencing transport mode selection................................................................ 13  \n2.2. Machine Learning essentials ......................................................................................... 14  \n2.3. Classification Algorithms & Techniques ...................................................................... 16  \n2.4. Class imbalance and resampling strategies ...................................................................22  \n2.5. Evaluation metrics .........................................................................................................22  \n2.6. Machine Learning applications in transport mode choice ............................................26  \n3. Case 1 – Thessaloniki ..........................................................................................................28  \n3.1. Methodology .................................................................................................................28  \n3.1.1 Python Libraries ......................................................................................................28  \n3.1.2 Data collection .........................................................................................................28  \n3.1.3 An","cbCaiknWWyxdCbi6","https://ap.wps.com/l/cbCaiknWWyxdCbi6","pdf",11509135,1,217,"English","en",105,"# Introduction\n# Literature Review\n## Factors influencing transport mode selection\n## Machine Learning essentials\n## Classification Algorithms & Techniques\n## Class imbalance and resampling strategies\n## Evaluation metrics\n## Machine Learning applications in transport mode choice\n# Case 1 – Thessaloniki\n## Methodology\n### Python Libraries\n### Data collection\n### Analysis procedure\n## RESULTS\n### Data preparation and cleaning\n### Exploratory Data Analysis\n### Data Preprocess\n### Decision Tree\n### Random Forest\n### XGBoost\n### Stacked Model\n### Model Comparison\n### Feature reduction and re-evaluation\n### Model selection\n### Model Explainer\n# Case 2 – Netherlands\n## Methodology\n### Data collection","[{\"question\":\"What topic does the thesis address?\",\"answer\":\"The thesis addresses transport mode choice by framing it as a machine learning classification problem using Python.\"},{\"question\":\"Which main machine learning aspects are covered in the literature review?\",\"answer\":\"It covers factors influencing transport mode selection, machine learning essentials, classification algorithms and techniques, class imbalance and resampling strategies, and evaluation metrics.\"},{\"question\":\"How are the models evaluated across the two cases?\",\"answer\":\"The document presents methodology and analysis steps for each case, trains multiple models (e.g., decision tree, random forest, XGBoost, stacked model), compares them using evaluation metrics, performs feature reduction, and selects the best model with an explainer.\"}]","Machine Learning and classification of transport mode choice using Python | 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topic does the thesis address?","Question",{"text":75,"@type":76},"The thesis addresses transport mode choice by framing it as a machine learning classification problem using Python.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which main machine learning aspects are covered in the literature review?",{"text":80,"@type":76},"It covers factors influencing transport mode selection, machine learning essentials, classification algorithms and techniques, class imbalance and resampling strategies, and evaluation metrics.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the models evaluated across the two cases?",{"text":84,"@type":76},"The document presents methodology and analysis steps for each case, trains multiple models (e.g., decision tree, random forest, XGBoost, stacked model), compares them using evaluation metrics, performs feature reduction, and selects the best model with an 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