Predictive Modeling using Data Mining Techniques for Crime Analysis

This paper presents a crime analysis and prediction system using data mining and machine learning techniques to interpret historical crime data and forecast future incidents. It integrates classification algorithms (Naive Bayes, Decision Trees, SVM), clustering (K-Means), linear regression, and association rule mining (Apriori) to identify crime patterns, hotspots, and trends. Advanced models like Artificial Neural Networks (ANNs) and ensemble methods (Random Forests) enhance prediction accuracy. Visualization tools such as heat maps and trend graphs aid in interpreting data and guiding law enforcement decisions. This system serves as a decision-support tool, enabling proactive crime prevention and smarter policing for safer communities.