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DOI: 10.14569/IJACSA.2022.0130760
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Assaying the Statistics of Crime Against Women in India using Provenance and Machine Learning Models

Author 1: Geetika Bhardwaj
Author 2: R. K. Bawa

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 7, 2022.

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Abstract: Now-a-days, the surging of crime against women is occurring at a startling rate in India. According to the National Commission for Women, there was a 46% increase in reports of crimes against women in the initial months of the year 2021 in comparison with the same period in 2020. However, to handle this problem, the need of the hour is to fetch relevant and timely information about the various types of crime taking place and make specific predictions based on the existing information to safeguard women from future predictable contingencies. AI and Machine learning mechanisms have become a powerful tool in predicting the crime rate in India under various crime categories by analyzing the crime patterns, crime–centric areas, and the comparative study of various crime categories. Hence, from 2001 to 2019, a women's crime-based dataset from NCRB has been used in this paper, which included various crime sub-categories, for instance; molestation, sexual harassment, rape, kidnapping, dowry deaths, cruelty to family, importation of girls, immortal traffic, sati prevention act, and others. To acquire a better understanding of the data, a framework has been created which makes use of provenance and machine learning algorithms on the dataset, which has been grouped based on several factors such as distribution of cases convicted or reported every year, safest and un-safest states for women in India, etc. Different machine learning algorithms, such as gradient boosting and its many versions, Random forest, and many more, have been used on the dataset. Their performances are evaluated using various metrics such as accuracy, recall, precision, F1 score, and root mean error square.

Keywords: Crime against women; provenance; scalar techniques; machine learning techniques; decision tree; random forest; gradient boosting; XgBoost; CatBoost; LightGBM

Geetika Bhardwaj and R. K. Bawa, “Assaying the Statistics of Crime Against Women in India using Provenance and Machine Learning Models” International Journal of Advanced Computer Science and Applications(IJACSA), 13(7), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130760

@article{Bhardwaj2022,
title = {Assaying the Statistics of Crime Against Women in India using Provenance and Machine Learning Models},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130760},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130760},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {7},
author = {Geetika Bhardwaj and R. K. Bawa}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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