Abstract
South Africa has been classified as one of the most homicidal, violent, and dangerous places across the globe. However, the two elements that pushed South Africa high in the crime rank are the rates of social violence and homicide. It was reported by Business Insider that South Africa is among the most top 15 ferocious nations on earth. By 1995, South Africa was rated the second highest in terms of murder. However, the crime rate has reduced for some years and suddenly rose again in recent years. Due to social violence and crime rates in South Africa, foreign investors are no longer interested in continuing or starting a business with the nation, and hence, its economy is declining. South Africa’s government is looking for solutions to the crime issue and to redeem the image of the country in terms of high crime ranking and boost the confidence of the investors. Many traditional approaches to data analysis in crime-related studies have been done in South Africa, but the machine learning approach has not been adequately considered. The police station and many other agencies that deal with crime hold a lot of databases that can be used to predict or analyze criminal happenings across the provinces of South Africa. This research work aimed at offering a solution to the problem by building a model that can predict crime. The machine learning approach shall be used to extract useful information from South Africa's nine provinces' crime data. A crime prediction system that can analyze and predict crime is proposed. To accomplish this, South Africa crime data on 27 crime categories were obtained from the popular data repository “Kaggle.” Diverse data analytics steps were applied to preprocess the datasets, and a machine learning algorithm (linear regression) was used to build a predictive model to analyze data and predict future crime. The appropriate authorities and security agencies in South Africa can have insight into the crime trends and alleviate them to encourage the foreign stakeholders to continue their businesses.
Highlights
South Africa has been classified as one of the most homicidal, violent, and dangerous places across the globe
Many traditional approaches to data analysis in crime-related studies have been done in South Africa, but the machine learning approach has not been adequately considered. e police station and many other agencies that deal with crime hold a lot of databases that can be used to predict or analyze criminal happenings across the provinces of South Africa. is research work aimed at offering a solution to the problem by building a model that can predict crime. e machine learning approach shall be used to extract useful information from South Africa’s nine provinces’ crime data
South Africa crime data on 27 crime categories were obtained from the popular data repository “Kaggle.” Diverse data analytics steps were applied to preprocess the datasets, and a machine learning algorithm was used to build a predictive model to analyze data and predict future crime. e appropriate authorities and security agencies in South Africa can have insight into the crime trends and alleviate them to encourage the foreign stakeholders to continue their businesses
Summary
South Africa has been classified as one of the most homicidal, violent, and dangerous places across the globe. E proposed linear regression predictive model was built based on South African’s crime data [7] with the 27 crime categories shown, population data [20], and province area (km2) depicted in Table 2 and density that was computed in this work. Is study aims to build a predictive model that can analyze the existing South Africa crime data, detect hidden patterns, and generate useful information that can be communicated to the
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Applied Computational Intelligence and Soft Computing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
