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This MSc research compares machine learning and deep learning algorithms for accident forecasting using Ireland’s accident dataset and evaluates whether hyperparameter optimisation improves ML performance. Models assessed include Random Forest, Decision Tree, XGBoost, Ridge Regression, and KNN, plus LSTM and Feedforward Neural Network. Performance is measured with MSE, RMSE, MAE, and R-squared. Results show hyperparameter tuning significantly boosts ML models; XGBoost leads (R-squared 0.96), while KNN underperforms. Among deep learning models, FNN surpasses LSTM, supporting more efficient resource allocation by policymakers and road safety authorities.",{"@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/comparative-study-of-machine-learning-and-deep-learning-approaches-for-predicting-irish-road-accidents-msc-research-project-data-analytics/290852/",{"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/comparative-study-of-machine-learning-and-deep-learning-approaches-for-predicting-irish-road-accidents-msc-research-project-data-analytics/290852.png","ImageObject",300,407,{"name":92,"@type":93},"Skyler","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",16,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the main goal of the research project?","Question",{"text":113,"@type":114},"To compare machine learning and deep learning approaches for predicting Irish road accidents and to determine the most effective technique.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which machine learning models are evaluated?",{"text":118,"@type":114},"Random Forest, Decision Tree, XGBoost, Ridge Regression, and KNN are evaluated on the Ireland accident dataset.",{"name":120,"@type":111,"acceptedAnswer":121},"What modeling metrics and key results are reported?",{"text":122,"@type":114},"Effectiveness is measured using MSE, RMSE, MAE, and R-squared. Hyperparameter optimisation improves ML performance significantly, with XGBoost achieving an R-squared of 0.96, while KNN underperforms; FNN performs better than LSTM for deep learning.","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},290852,1789642807,{"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":145,"preview_page_count":4},2336464648746,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Comparative Study of Machine Learning and Deep Learning Approaches for Predicting Irish Road Accidents  \nMSc Research Project  \nData Analytics  \nKarthika Rajan  \nStudent ID: X21122920  \nSchool of Computing National College of Ireland  \nSupervisor: Muslim Jameel Syed  \nNational College of Ireland Project Submission Sheet School of Computing  \n| Student Name: | Karthika Rajan |\n| --- | --- |\n| Student ID: | X21122920 |\n| Programme: | Data Analytics |\n| Year: | 2023 |\n| Module: | MSc Research Project |\n| Supervisor: | Muslim Jameel Syed |\n| Submission Due Date: | 18/09/2023 |\n| Project Title: | Comparative Study of Machine Learning and Deep Learning Approaches for Predicting Irish Road Accidents |\n| Word Count: | 9000 |\n| Page Count: | 33 |\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: | Karthika Rajan |\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\nComparative Study of Machine Learning and Deep Learning Approaches for Predicting Irish Road  \nAccidents  \nKarthika Rajan  \nX21122920  \nAbstract  \nIn past few years, the crashes on roads in Ireland have become an increasing concern. According to Road Safety Authority of Ireland (RSA), number of crashes in Ireland has increased by 13 percentage, demonstrating the need for further measures for lowering the crash rate. The purpose of this research is to perform a comparison between several machine learning and deep learning algorithms on accident dataset of Ireland in order to determine the ideal technique for accident forecasting. This project also intends to determine if hyperparameter optimisation can improve the performance of the ML techniques. The evaluated machine learning algorithms are Random Forest, Decision Tree, XGBoost, Ridge Regression and KNN, whereas the deep learning models are Long Short-Term Memory (LSTM) and Feedforward Neural Network (FNN) . Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R-squared score are used to measure the effectiveness of the models. The results indicate that the hyperparameter optimisation enhanced the performance of the machine learning models significantly. After tuning their hyperparameters, XGBoost outperformed other models followed by Random Forest and Ridge Regression. XGBoost has got an R-squared score of 0.96. KNN algorithm underperformed when compared to other models. For deep learning models, FNN model performed better than LSTM algorithm. This study offers important insights into the application of machine learning and deep learning models for prediction of traffic accidents in Ireland. The results can definitely help the policymakers and the road safety authorities to allocate resources more efficiently. By this it is possible to reduce the accidents and can enhance","cbCaiifkbJf4Vu21","https://ap.wps.com/l/cbCaiifkbJf4Vu21","pdf",1537063,35,"English","# 1 Introduction\n## 1.1 Background and Motivation","[{\"question\":\"What is the main goal of the research project?\",\"answer\":\"To compare machine learning and deep learning approaches for predicting Irish road accidents and to determine the most effective technique.\"},{\"question\":\"Which machine learning models are evaluated?\",\"answer\":\"Random Forest, Decision Tree, XGBoost, Ridge Regression, and KNN are evaluated on the Ireland accident dataset.\"},{\"question\":\"What modeling metrics and key results are reported?\",\"answer\":\"Effectiveness is measured using MSE, RMSE, MAE, and R-squared. Hyperparameter optimisation improves ML performance significantly, with XGBoost achieving an R-squared of 0.96, while KNN underperforms; FNN performs better than LSTM for deep learning.\"}]","Comparative Study of Machine Learning and Deep Learning Approaches for Predicting Irish Road Accidents - MSc Research Project - Data Analytics | PDF",88]