Crime Pattern Analysis Using Machine Learning

M.Nagakeerthi ¹, G.Lalitha ²

¹ Assistant Professor ,² MCA Final Year Student,

 Master of Computer Applications, Sanketika Vidya Parishad Engineering College, Vishakhapatnam, Andhra Pradesh, India

keerthi.punni@gmail.com, gogadalalitha7@gmail.com

Abstract:

The Crime Pattern Analysis using Machine Learning system is a web-based application designed to analyze historical crime data and predict crime patterns using machine learning algorithms. The system enables users to provide crime-related parameters such as city, crime type, and year, which are processed using trained machine learning models such as Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). These algorithms learn significant patterns from historical crime datasets and identify crime trends, classify crime categories, and estimate the likelihood of crime occurrences in different locations. Based on this analysis, the system generates crime predictions and provides meaningful insights through an interactive interface. The application is developed using Python, Flask, Scikit-learn, Pandas, NumPy, Matplotlib, HTML, CSS, and Bootstrap, offering a simple and user-friendly platform for crime analysis and prediction. This system improves prediction accuracy, reduces manual analysis, supports law enforcement agencies in decision-making, saves time, and provides a reliable solution for data-driven crime prevention and public safety.

Keywords: Crime Pattern Analysis, Machine Learning, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Crime Prediction, Data Analytics, Flask, Python, Crime Classification, Public Safety.