利用机器学习预测犯罪

Sridharan S, Srish N, Vigneswaran S, Santhi P
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引用次数: 0

摘要

对犯罪模式和趋势进行研究,以找出潜在问题和预防犯罪的潜在解决方案的过程被称为犯罪分析。这包括使用统计分析、地理制图和其他方法来分析其所在地区的犯罪类型和范围。犯罪分析还包括创建预测模型,利用以前的数据来预测未来的犯罪趋势。执法部门可以通过评估犯罪数据和发现犯罪趋势,更有效地分配资源,有针对性地采取减少犯罪和提高公共安全的措施。为了进行预测,这些数据被输入线性回归和随机森林等算法。利用 2001 年至 2016 年的数据,对印度各邦以及所有邦的犯罪类型进行了预测。使用简单的可视化图表来表示这些预测。这些算法的一个关键特点是识别趋势变化的年份,以提高预测的准确性。主要目的是利用 2001 年至 2016 年的数据集预测 2017 年至 2020 年的犯罪案件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Crime Prediction using Machine Learning
The process of researching crime patterns and trends in order to find underlying issues and potential solutions to crime prevention is known as crime analysis. This includes using statistical analysis, geographic mapping, and other approaches of type and scope of crime in their areas. Crime analysis can also entail the creation of predictive models that use previous data to anticipate future crime tendencies. Law enforcement authorities can more efficiently allocate resources and target initiatives to reduce crime and increase public safety by evaluating crime data and finding trends. For prediction, this data was fed into algorithms such as Linear Regression and Random Forest. Using data from 2001 to 2016, crime-type projections are made for each state as well as all states in India. Simple visualisation charts are used to represent these predictions. One critical feature of these algorithms is identifying the trend-changing year in order to boost the accuracy of the predictions. The main aim is to predict crime cases from 2017 to 2020 by using the dataset from 2001 to 2016.
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