A Comparative Analysis of Machine Learning Algorithms for Crime Rate Prediction

Abstract

This study presents a detailed analysis and framework utilizing machine learning techniques to understand the occurrences of crimes against women in India. The key methodologies include data preprocessing, feature selection, and model selection whichwould enhance the accuracy of the models. Various supervised learning algorithms such as logistic regression, naive Bayes, stochastic gradient descent, K-nearest neighbor, decision tree, random forest, and extreme gradient booster have been used and compared to understand which algorithm would be the most capable. The findings and approach discussed in this study can be used to mitigate the risks, enhance victim support systems, and strengthen preventive measures to reduce crimes against women. This study would also contribute to combating gender-based violence and foster a safer and more inclusive environment for women in India. © 2024 IEEE.