A crime is a punishable offence that is harmful for an individual and his
society. It is obvious to comprehend the patterns of criminal activity to
prevent them. Research can help society to prevent and solve crime activates.
Study shows that only 10 percent offenders commits 50 percent of the total
offences. The enforcement team can respond faster if they have early
information and pre-knowledge about crime activities of the different points of
a city. In this paper, supervised learning technique is used to predict crimes
with better accuracy. The proposed system predicts crimes by analyzing data-set
that contains records of previously committed crimes and their patterns. The
system stands on two main algorithms - i) decision tree, and ii) k-nearest
neighbor. Random Forest algorithm and Adaboost are used to increase the
accuracy of the prediction. Finally, oversampling is used for better accuracy.
The proposed system is feed with a criminal-activity data set of twelve years
of San Francisco city.

本研究利用监督学习技术预测犯罪活动，通过分析先前犯罪活动记录和模式，该系统基于决策树和 K 最近邻算法进行预测，同时使用随机森林算法和 Adaboost 提高准确性，并使用过采样来提高准确性。