Crime Data Analysis Using Machine Learning Models

  • Telugu Maddileti,Vaddemani Sai Madhav, K V Sai Sashank,G. Shriphad Rao

Abstract

The criminal cases in India are increasing rapidly due to which number of cases pending are also piling up. This continuous increase in the criminal cases is proving to be difficult to be classified and to be solved. Recognizing the criminal activity patterns of a place is important in order to prevent it from happening. The crime solving agencies can do a better work if they have a good idea of the pattern of criminal activities that are happening in a particular area. This can be done by using machine learning by employing different algorithms to find the patterns of the criminal activities in a particular area. This paper uses crime data set and predicts the types of crimes in a particular area which helps in speeding up the classification of criminal cases and proceed accordingly. This paper uses the data of past 18 years that is collected from various trusted sources. Data pre-processing is as important as final prediction, this paper used feature selection, removing null values and label encoding to clean and nourish the data. This research gives an efficient machine leaning model for predicting the next criminal case. Various Machine learning models such as Logistic Regression, Decision Tree Classification, and Random Forest Classification were used to find the most efficient model to predict the type of crime at a particular location This paper discusses the about existing system which uses K-nearest neighbour to predict next type of crime at a particular location, and also shows how the proposed system is better than the present existing system. This paper compares many machine learning models among themselves to find most efficient machine learning to tackle this problem.

Published
2020-05-15
How to Cite
Telugu Maddileti,Vaddemani Sai Madhav, K V Sai Sashank,G. Shriphad Rao. (2020). Crime Data Analysis Using Machine Learning Models. International Journal of Advanced Science and Technology, 29(9s), 3260 - 3268. Retrieved from https://sersc.org/journals/index.php/IJAST/article/view/15887