Crime Data Analysis Using Naive Bayes Classification and Least Square Estimation with MapReduce

Nowadays, digital evidence plays a vital role in criminal investigations and arraignments. Digital criminal Investigators can also use this as an opportunity if the vast amount of data is a current trial. Assess constructive and constructive data and advice from the defendant proof behind the crime in terms of issues. Identifying criminal or criminal activity is a big deal because it connects certain data sets. It set an innovative law framework to quickly and accurately solve problems within the law's boundary. In this regard, the machine learning approach Naive Bayes classification for digital criminology data sets is to identify criminals. The Naive Baye classification process is used for digital criminology data application. To approximate square estimate for data sets of digital criminology subgroups. Also, support the Hadoop Big Data System Understanding Map with Reduce programming with the Naive Bayes classifier. The experiment result was a huge accumulated failure in the data quality. Based on these data, the estimation parameter of the statistical model is reached. The least-square estimate estimates the parameters that deal with the statistical model in the experimental result.