Digital Repository
Road Accident Severity Prediction in Mauritius using Supervised Machine Learning Algorithms
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| dc.contributor.author | Sowdagur, Jameel Ahmad | |
| dc.contributor.author | Rozbully-Sowdagur, B. Tawheeda B | |
| dc.contributor.author | Suddul, Geerish | |
| dc.date.accessioned | 2022-02-25T04:24:04Z | |
| dc.date.available | 2022-02-25T04:24:04Z | |
| dc.date.issued | 2021 | |
| dc.identifier.citation | Sowdagur Jameel Ahmad, Rozbully-Sowdagur B. Tawheeda B, Suddul Geerish (2021), Road Accident Severity Prediction in Mauritius using Supervised Machine Learning Algorithms, International Conference on Advances in Computing and Technology (ICACT–2021) Faculty of Computing and Technology (FCT), University of Kelaniya, Sri Lanka 153-157 | en_US |
| dc.identifier.uri | http://repository.kln.ac.lk/handle/123456789/24512 | |
| dc.description.abstract | Road accidents with high severities are a major concern worldwide, imposing serious problems to the socio-economic development. Several techniques exist to analyse road traffic accidents to improve road safety performance. Machine learning and data mining which are novel approaches are proposed in this study to predict accident severity. Support Vector Machine (SVM), Gradient Boosting (GB), Logistic Regression (LR), Random Forest (RF) and Naïve Bayes (NB) were applied to perform effective data analysis for informed decisions using Python programming language. The gradient boosting outperformed all the other models in predicting the severity outcomes, yielding an overall accuracy of 83.2% and an AUC of 83.9% | en_US |
| dc.publisher | Faculty of Computing and Technology (FCT), University of Kelaniya, Sri Lanka | en_US |
| dc.subject | Data mining, machine learning, accident severity. | en_US |
| dc.subject | . | en_US |
| dc.title | Road Accident Severity Prediction in Mauritius using Supervised Machine Learning Algorithms | en_US |
