A Review on Forecasting Crime against women in India using Machine Learning Approaches
DOI:
https://doi.org/10.32628/CSEIT228666Keywords:
Crime against women, Data Mining, Region, Machine Learning, Forecasting, Support Vector Machine, K-nearest Neighbor, Random Forest, Decision Tree, Navier Bayes.Abstract
Crimes against women have become a global problem, and many governments are striving to curb them. The National Crime Records Bureau indicates that crimes against women have risen substantially. In June, NCW received the most crime complaints against women in eight months. The Indian government is interested in finding a solution to this problem and promoting social progress. Each year, crime reports generate a vast amount of data, which is collated. This information may help us evaluate and anticipate criminal behavior and reduce criminal activity. Data analysis involves assessing, cleansing, manipulating, and modelling data to draw conclusions and enhance decision-making. This research uses supervision learning to analyze the Indian women's criminal examination. The police department received crime reports. Anomalies, invalid locations, longitudes, and scopes were created in advance. The study was meant to breakdown women's crimes by kind and district and produce crime heat maps. The results help decision makers predict and prevent crimes against women. Applying Find the geographical criminal hotspot and the kind of crime, such as murder, rape, sexual assault, beating, dowry threats by the husband or his family, immoral trafficking, stalking, etc.
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