Crime Data Analysis Using Machine Learning

Crime Data Analysis Using Machine Learning

B. Varun Kumar , B. Koushik , K. Vivek, A. Pranay Kumar, P. Vamshi Krishna

Abstract-

This project focuses on utilizing machine learning (ML) techniques for crime data analysis, aiming to develop predictive models for crime prediction. Beginning with the collection of comprehensive datasets containing crime types, locations, times, and socio-economic factors, the data is preprocessed to handle inconsistencies and outliers, followed by feature engineering to extract relevant patterns. Various Machine Learning algorithms, including decision trees, Gaussian Naive Bayes, Logistic regression, and KNN [K-nearest neighbor], are then employed to train predictive models on historical crime data, with evaluations conducted using metrics such as accuracy, precision, recall, and F1-score. Once validated, these models are deployed for real-time predictions on future crime occurrences, providing valuable insights for law enforcement agencies and policymakers to allocate resources effectively and implement proactive measures. Ultimately, this project showcases the potential of Machine Learning techniques in enhancing public safety and security by analyzing crime data and informing decision-making processes.

 Key words: Data Analysis, Crime prediction, Gaussian Naive Bayes, Crime prevention, Logistic regression, Predictive analytics.