To be better prepared to respond to criminal activity, it is important to understand patterns in crime. In our project, we analyze crime data from the city of Indore, scraped from publicly available website of Indore Police. At the outset, the task is to predict which category of crime is most likely to occur given a time and place in Indore. The use of AI and machine learning to detect crime via sound or cameras currently exists, is proven to work, and expected to continue to expand. The use of AI/ML in predicting crimes or an individual’s likelihood for committing a crime has promise but is still more of an unknown. The biggest challenge will probably be “proving” to politicians that it works. When a system is designed to stop something from happening, it is difficult to prove the negative. Companies that are directly involved in providing governments with AI tools to monitor areas or predict crime will likely benefit from a positive feedback loop. Improvements in crime prevention technology will likely spur increased total spending on this technology. We also attempt to make our classification task more meaningful by merging multiple classes into larger classes. Finally, we report and reflect on our results with different classifiers, and dwell on avenues for future work.