[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290853-105":59,"doc-detail-290853-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","pre-owned-bike-price-prediction-using-machine-learning-msc-research-project-data-analytics","Pre-Owned Bike Price Prediction Using Machine Learning - MSc Research Project Data Analytics","","Pre-Owned Bike Price Prediction Using Machine Learning presents an MSc Research Project in Data Analytics focused on predicting used bikes’ resale value with machine learning models. The study specifies a Windows 11, R Studio environment, R programming language, and listed package dependencies. It uses a Kaggle bike price dataset containing over 5063 records from India, with features such as model name, model year, kms driven, owner location, mileage/power, and price. The research question evaluates how effectively different models forecast resale price, comparing linear regression, elastic regression, SVR, random forest, and XG Boost using MAPE and RMSE scores across trials with multiple seeds and split ratios.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/pre-owned-bike-price-prediction-using-machine-learning-msc-research-project-data-analytics/290853/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/pre-owned-bike-price-prediction-using-machine-learning-msc-research-project-data-analytics/290853.png","ImageObject",300,407,{"name":92,"@type":93},"Skyler","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the main research question of this project?","Question",{"text":113,"@type":114},"How effective are the machine learning models in accurately predicting the resale value of the used bikes?","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which machine learning models are compared to forecast used bike prices?",{"text":118,"@type":114},"The study compares Linear Regression, Elastic Regression, Support Vector Regressor, Random Forest, and XG Boost.",{"name":120,"@type":111,"acceptedAnswer":121},"What metrics are used to evaluate model performance?",{"text":122,"@type":114},"Model performance is evaluated using MAPE and RMSE scores reported from the model runs.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290853,1789642814,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":36,"preview_page_count":4},2336464648746,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Pre-Owned Bike Price Prediction Using Machine Learning  \nMSc Research Project Data Analytics  \nKeerthana Sathyanarayanan  \nStudent ID: x21195234  \nSchool of Computing National College of Ireland  \nSupervisor: Dr. Anh Duong Trinh  \nNational College of Ireland Project Submission Sheet School of Computing  \n| Student Name: | Keerthana Sathyanarayanan |\n| --- | --- |\n| Student ID: | x21195234 |\n| Programme: | Data Analytics |\n| Year: | 2023 |\n| Module: | MSc Research Project |\n| Supervisor: | Dr. Anh Duong Trinh |\n| Submission Due Date: | 14/08/2023 |\n| Project Title: | Pre-Owned Bike Price Prediction Using Machine Learning |\n| Word Count: | 1130 |\n| Page Count: | 14 |\n\nI hereby certify that the information contained in this (my submission) is information pertaining to research I conducted for this project. All information other than my own contribution will be fully referenced and listed in the relevant bibliography section at the rear of the project.  \nALL internet material must be referenced in the bibliography section. Students are required to use the Referencing Standard specified in the report template. To use other author’s written or electronic work is illegal (plagiarism) and may result in disciplinary action.  \n\n| Signature: | Keerthana Sathyanarayanan |\n| --- | --- |\n| Date: | 17th September 2023 |\n\nPLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST:  \n\n| Attach a completed copy of this sheet to each project (including multiple copies) . | ✓ |\n| --- | --- |\n| Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies) . | ✓ |\n| You must ensure that you retain a HARD COPY of the project, both foryour own reference and in case a project is lost or mislaid. It is not sufficient to keep a copy on computer. | ✓ |\n\nAssignments that are submitted to the Programme Coordinator office must be placed into the assignment box located outside the office.  \n\n| Office Use Only |  |\n| --- | --- |\n| Signature: |  |\n| Date: |  |\n| Penalty Applied (if applicable): |  |\n\nPre-Owned Bike Price Prediction Using Machine  \nLearning  \nKeerthana Sathyanarayanan  \nx21195234  \n1 Introduction  \nThis configuration manual can be used to achieve same objectives as the work conducted generating equivalent results. It includes hardware and software specifications, datasetsource, model implementation code and model evaluation code appended.  \n2 Hardware and Software Specifications:  \n• CPU: Processor used for the research is 11th Gen Intel(R) Core(TM) i5-11320H @ 3.20GHz 2 .50 GHz.  \n• RAM: RAM used for the study is 16GB.  \n• Storage: 477 GB  \n• GPU: NVIDIA GeForce MX450  \n• Operating System: 64-bit operating system, x64-based processor. Windows 11 is used.  \n• Environment: R Studio.  \n• The programming language used is R programming.  \n• The package dependencies are tidyverse, corrplot, ggplot2, lubridate, gridExtra, caTools, GGally, randomForest, caret, ISLR, xgboost.  \n3 Dataset Used:  \n• The dataset used in this project is a bike price prediction dataset.  \n• It includes various features related to bikes, such as model name, model year, kms driven, owner location, mileage power, price.  \n• It comprises over 5063 records providing data from all across India.  \n• The source of the dataset is Kaggle. 1  \n1[https://www.kaggle.com/datasets/vinayjain449/bike-prediction-with-linear-regression](https://www.kaggle.com/datasets/vinayjain449/bike-prediction-with-linear-regression)  \n4 Research Question:  \nHow effective are the machine learning models in accurately predicting the resale value of the used bikes?  \n5 Objective:  \nUsing the five potential machine learning models, a comparative study to identify the best models for forecasting the price of used bikes is conducted. Models used include Linear Regression, Elastic Regression, Support Vector Regressor, Random Forest, and XG Boost.  \n6 Experiment Design:  \n6 shows the process flow of the experiment.  \n7 Implementation  \n• Step 1- Run the code from figur","cbCaisw3LY9IQcln","https://ap.wps.com/l/cbCaisw3LY9IQcln","pdf",2954713,16,"English","# 1 Introduction\n## 2 Hardware and Software Specifications\n## 3 Dataset Used\n## 4 Research Question\n## 5 Objective\n## 6 Experiment Design\n## 7 Implementation\n## 7.1 Linear Regression\n## 7.2 Random Forest\n## 7.3 Support Vector Regressor\n## 7.4 Elastic Regression\n## 7.5 XG Boost\n# 8 Evaluation","[{\"question\":\"What is the main research question of this project?\",\"answer\":\"How effective are the machine learning models in accurately predicting the resale value of the used bikes?\"},{\"question\":\"Which machine learning models are compared to forecast used bike prices?\",\"answer\":\"The study compares Linear Regression, Elastic Regression, Support Vector Regressor, Random Forest, and XG Boost.\"},{\"question\":\"What metrics are used to evaluate model performance?\",\"answer\":\"Model performance is evaluated using MAPE and RMSE scores reported from the model runs.\"}]","Pre-Owned Bike Price Prediction Using Machine Learning - MSc Research Project Data Analytics | PDF"]