TY -的A2 Daneshvar Rouyendegh (b . Erdebilli) Babak盟——Obagbuwa Ibidun Christiana盟——Abidoye Ademola p . PY - 2021 DA - 2021/06/09 TI -南非犯罪可视化、趋势分析、线性回归和预测使用机器学习技术SP - 5537902六世- 2021 AB -南非被列为最杀气腾腾的之一,在全球范围内暴力和危险的地方。然而,这两个元素,推动南非的犯罪等级率高的社会暴力和谋杀。这是商业内幕的报道,南非是地球上最前15名凶猛的国家之一。到1995年,南非被评为第二高的谋杀。然而,犯罪率减少了一些年,近年来突然再次上升。由于社会暴力和犯罪率在南非,外国投资者不再感兴趣的持续或创业的国家,因此,它的经济正在下降。南非政府正在寻找解决犯罪问题和赎回的国家形象的高犯罪率排名和提振投资者的信心。许多传统的数据分析方法在犯罪相关研究已经完成在南非,但机器学习方法没有充分考虑。警察局和许多其他机构处理犯罪持有大量的数据库,可以用来预测或分析犯罪事件在南非的省份。本研究工作旨在提供一个解决问题的办法,通过建立模型,该模型可以预测犯罪。 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. SN - 1687-9724 UR - https://doi.org/10.1155/2021/5537902 DO - 10.1155/2021/5537902 JF - Applied Computational Intelligence and Soft Computing PB - Hindawi KW - ER -