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  Using Data Mining Technique to Analyze Crime of Bangladesh  
  Authors : Md. Abdul Awal; Jakaria Rabbi; Imran Rana
  Cite as:


Crime is classically unforeseeable and a social nuisance. It is not necessarily random, but neither does it take place consistently in space or time. In the recent past, there has been an enormous increase in the rate of crime, hence the significance of task to predict, prevent or solve the crimes. In this case, machine learning and data mining techniques can play an important role to discover future trends and patterns of crime. In this paper, linear regression model is used to forecast future crime trends of Bangladesh. The real dataset of crime is collected from the website of Bangladesh police. The dataset contains aggregated counts of different types of crime. Then the linear regression model is trained on this dataset. After training the model, crime forecasting is done for dacoit, robbery, murder, women & child repression, kidnapping, burglary and theft for different region of Bangladesh.


Published In : IJCSN Journal Volume 6, Issue 4

Date of Publication : August 2017

Pages : 489-494

Figures :03

Tables : 05


Md. Abdul Awal : Department of Computer Science and Engineering, Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh.

Jakaria Rabbi : Department of Computer Science and Engineering, Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh.

Imran Rana : Department of Computer Science and Engineering, Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh.


Data Mining, Crime Forecasting, Linear Regression, Gradient Descent

At present, Data mining has become a vital part of crime detection and prevention in many countries. There are other applications of data mining in the domain of law enforcement such as determining criminal "hot spots", creating criminal profiles, and learning crime trends to hold the pledge of abating crime related problem. In this paper, data mining technique is used to forecast future crime trends of Bangladesh. For this purpose, linear regression model is trained by crime data of previous years. After training linear regression, different types of crime are forecasted for the year of 2018. All the results are shown through the table 2 to table 5. Table 1 shows that, how accurate the liner regression is to forecast future crime trends of Bangladesh. From the experimental result it is also seen that, most of the crimes are increasing with the growth of population. The results of this data mining could potentially be used to lessen and even prevent crime for the forthcoming years. The extension of this research work is to forecast the location of crime occurrence, so that prior actions can be taken to prevent crime.


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