Crime Pridiction Using K-Nearest Neighboring Algorithm

Computer Engineering

In this era of recent times, crime has become an evident way of making people and society under trouble. An increasing crime factor leads to an imbalance in the constituency of a country. In order to analyze and have a response ahead this type of criminal activity, it is necessary to understand the crime patterns. This study imposes one such crime pattern analysis by using crime data obtained from Kaggle open source which in turn is used for the prediction of most recently occurring crimes. The major aspect of this project is to estimate which type of crime contributes the most along with time period and location where it has happened. Some machine learning algorithms such as Naive Bayes is implied in this work in order to classify among various crime patterns and the accuracy achieved was comparatively high when compared to precomposed works.

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