Crime Risk Prediction and Analysis using Machine Learning

Crime analysis involves identifying and analyzing patterns in crime data. By focusing on specific types of crimes, we aim to identify where they are most likely to occur. This system helps in predicting the higher probability crime occurrence regions and visualize maximum crime-prone areas. By using the concept of machine learning, we can analyze previously known useful crime information available through official sources. To accomplish this, we obtain raw data from the Kaggle website. We propose a system which can analyze and classify communities as either high Crime or low Crime based on their socioeconomic features. On this preprocessed dataset, by applying various machine learning algorithms, we create a model which analyzes the data and helps to identify the important factors associated with high crime rates. Multiple models such as Logistic Regression, Decision tree, SVM, Random Forest are applied and compared to choose the best-performing one based on accuracy and other metrics. Data visualization can assist in crime prevention by using crime analytics. These Insights are displayed in the form of graphs for a better understanding. Towards this goal, crime analysis is being suggested as an approach between computer science and criminal justice to develop a machine learning framework that can help prevent crime occurrences.