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Edoka, Nelson Omonigho (2020) Crime Incidents Classification Using Supervised Machine Learning Techniques: Chicago. Masters thesis, Dublin, National College of Ireland.
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Abstract
Crime is a difficult issue faced by most nations on the planet today. The powerlessness to control crime has prompted genuine drop down in the economy of the nation, loss of lives and property. The growing need to mitigate crimes gave rise to this research work by applying data mining techniques from the data obtained from Chicago crime porter to break down the different crimes and build up a model that was able to classify these crimes, looking at the different models as far as execution to check how well the crimes were classified, and in return help the government and law enforcement agencies get an insight of the most common type of crimes they come across daily and enable them to take careful steps to overcome this criminal activities. Logistic Regression, K Nearest Neighbors, Naïve Bayes, Decision Tree Classifier and XGBoost were the five supervised classification machine learning techniques used to handle this issue. Resampling techniques was then applied on the crime data to deal with the problem of imbalanced data. The outcomes obtained from the developed models indicated that Decision Tree Classifier, and XGBoost acquired an accuracy of 99.6% and 97.3% respectively, Logistic Regression, K Nearest Neighbors, and Naïve Bayes acquired an accuracy of 39.8%, 81.2%, and 67.5% in classifying crime incidents in Chicago
| Item Type: | Thesis (Masters) |
|---|---|
| Subjects: | H Social Sciences > HN Social history and conditions. Social problems. Social reform Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software |
| Divisions: | School of Computing > Master of Science in Data Analytics |
| Depositing User: | Dan English |
| Date Deposited: | 23 Jun 2020 11:08 |
| Last Modified: | 23 Jun 2020 11:08 |
| URI: | https://norma.ncirl.ie/id/eprint/4315 |
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