Crime is a widespread social problem that crosses cultural, socio eco- nomic, and local communities. Criminal activity has a significant effect on families, and society at large. Projects pertaining to crime are necessary be- because it is urgently necessary to address and lessen the detrimental effects of criminal activity. A significant component of crime analysis is data mining. Machine learning and data mining are essential tools for reducing crime because giving legislators and law enforcement organizations strong tools to evaluate enormous volumes of data, spot trends, and anticipate future criminal activity. Supervised, unsupervised, and reinforcement learning algorithms are examples of machine learning algorithms that further improve the efficacy of crime reduction. Machine learning algorithms can estimate the likelihood of criminal episodes occurring in particular places and time periods by training predictive models on previous crime data. Because predictive capacity, law enforcement organizations are able to take preventative action before crimes occur by stepping up patrols or focusing on specific areas. Algorithms that use machine learning techniques can help identify people or groups who are more likely to commit crimes. In this work, we identified crime analysis using linear regression and support vector machines (SVM). These algorithms are used to forecast the rate of crime and the number of crimes that will happen in the near future. Law enforcement agencies can utilize this information to reduce crime and allocate resources more effectively.