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By: Ravinder Singh
Student, Department of Computer Science Engineering, Birla Institute of Technology and Science, Pilani, Rajasthan, India
The classification of crime data has grown more and more important for public safety and law enforcement due to the increasing demand for precise and effective predictive models. To classify crime data, this study assesses and contrasts the effectiveness of several machine learning (ML) and deep learning (DL) models, such as decision trees, support vector machines, neural networks, and convolutional neural networks. The study evaluates the models’ accuracy, efficiency, and scalability using real-world crime datasets. This study explores the performance of various Machine Learning (ML) and Deep Learning (DL) models in the classification of crime data across multiple datasets. The model’s accuracy ranged from 83% to 98%, and evaluation criteria like accuracy, confusion matrix, precision, recall, and F1-score were employed to gauge its performance. The analysis highlights the effectiveness of models like Random Forest, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), and TabNet in classification tasks, emphasizing the importance of model selection, hyperparameter tuning, and data consistency. Additionally, a web application was developed to visualize crime data using interactive dashboards, enhancing the practical utility of the models. The findings underscore the potential of ML and DL models in crime data analysis, offering valuable insights for model selection and deployment.
Keywords: Artificial Intelligence, Machine Learning, Deep Learning, TabNet, Crime Pattern Theory
Citation:
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