AI-Driven Crime Prediction Using Machine Learning and Flask

Abstract: Crime analysis plays a vital role in ensuring public safety and effective law enforcement. The increasing rate of crimes makes it necessary to analyze large amounts of crime data efficiently. Traditional methods mainly depend on manual analysis of historical records, which are time-consuming and lack predictive capabilities. As a result, identifying crime patterns and trends becomes difficult. To overcome these limitations, a machine learning-based system is proposed. The system is developed using a Flask-based web application and processes crime datasets containing attributes such as crime type, location, and time. Data pre-processing techniques are applied to clean and prepare the data for analysis. Machine learning algorithms are then used to extract meaningful patterns from the data. Classification techniques help in predicting the type of crime, while clustering methods are used to identify crime-prone areas. The system also provides visualizations such as graphs and heatmaps, which make it easier to understand crime patterns and trends. By integrating data processing, prediction, and visualization into a single platform, the system improves decision-making and supports proactive crime prevention.

Keywords: Crime Analysis, Machine Learning, Crime Prediction, Flask, Data Pre-processing, Classification, Clustering, Data Visualization, HeatMaps