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A Comparative Study of Crimes Against Women Based on Machine Learning using Big Data techniques

EasyChair Preprint no. 4376

9 pagesDate: October 13, 2020

Abstract

Violence against women has become a prominent topic of discussion in India in recent years. Our Government and the Media have placed great focus in this issue due to continuously increasing crime rate. There many crimes committed in different states of India. This project mainly focuses on machine learning in pattern recognition for analysis of the patterns through Indian states for crime against women. Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.This helps in the proper analysis of data related to the crime issues against women and the same could provide an aid to the government in effective policy making for preventive measures. This project describes how to use machine learning to recognize crimes of different states and display them in a explicitly understandable format which could help in the generation of doable preventive measures. In this project, there would be conception of clustering methodology along with machine learning algorithms like: Cleaning the Dataset, Clustering. The Performance of each algorithm is analyzed and the best algorithm is found out which is having maximum accuracy of crime detection.The basic aim is to provide with an online application that could generate a quick and analyzed view of crime against women scenario in India.

Keyphrases: cleaning, Clustering, data analysis, data set, machine learning

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:4376,
  author = {Shivani Mishra and Suraj Kumar},
  title = {A Comparative Study of Crimes Against Women Based on Machine Learning using Big Data techniques},
  howpublished = {EasyChair Preprint no. 4376},

  year = {EasyChair, 2020}}
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