CRIME TYPE AND OCCURANCE PREDICTION USING MACHINE LEARNING

Abstract

Crime prediction has become an important application of machine learning in improving public safety and supporting law enforcement agencies. This project focuses on predicting crime types and their occurrence using machine learning techniques based on historical crime data. The system analyzes various features such as location, time, date, and crime categories to identify patterns and trends in criminal activities. By applying classification algorithms such as Decision Tree, Random Forest, and Logistic Regression, the model predicts the type of crime that is likely to occur in a specific area and time. The system also includes data preprocessing steps such as data cleaning, normalization, and feature selection to improve model accuracy. Visualization techniques are used to represent crime distribution across different locations, time periods, and categories, helping users understand crime patterns effectively. The trained model can assist law enforcement agencies in taking preventive measures by identifying high-risk areas and times. Experimental results show that machine learning models can achieve high accuracy in predicting crime types and occurrences, depending on the quality and size of the dataset. However, challenges such as data imbalance, missing values, and dynamic crime behavior may affect prediction performance. Overall, this project demonstrates the potential of machine learning in crime analysis and prediction, contributing to smarter policing and safer communities.