Today, Predictive analysis is considered a viable and practical procedure to distinguish the probability of future results dependent on authentic information. Much research has been done into crime prediction, and more sophisticated technology is arriving at the new front with new technology. Predictive analysis is a feasible and valuable strategy for predicting future outcomes based on facts. Crime prediction has been studied extensively, and new technologies are emerging. Given the variety of criminal classification systems, augmentation is necessary. With clustering, forecasting, and machine learning, the system's accuracy from tools to increase criminal analysis has reached a distinct level in observation prediction.

          The problem is a cluster of timer series data, with two limitations: handling the missing value and high dimensional data. Some algorithms and data are used in crime prediction research; we will expand the scope of that restricted research with empirical machine learning analysis—classification problem with limited algorithms. For crime data, measurement of such characteristics is impossible due to the lack of available data and accurate information.