Crime Data Analysis and Prediction of Suspect identity using Machine Learning Approach

Dr S Govinda Rao; Dr P Vara Prasada Rao; Dr P Chandrasekhar Reddy

Crime Data Analysis and Prediction of Suspect identity using Machine Learning Approach

Authors: Dr S Govinda Rao, Dr P Vara Prasada Rao, Dr P Chandrasekhar Reddy

Unique Paper ID: 157809
Volume: 5
Issue: 4
PageNo: 401-404

Keywords: Multilinear Regression; K-Neighbors Classifier; Artificial Neural Networks

Abstract:
Wrongdoing is one of the most transcendent and disturbing parts of our general public and its counteraction is a crucial errand. A calculated way to deal with distinguishing and looking at examples and patterns in wrongdoing is through wrongdoing examination. This model expects to build the productivity of wrongdoing examination frameworks. This model predicts the attributes of the guilty party who is probably going to be associated with perpetrating the wrongdoing and perceives wrongdoing designs from derivations assembled from the crime location. This work has two significant angles: Wrongdoing Investigation and Forecast of culprit personality. The Wrongdoing Examination stage distinguishes the quantity of inexplicable violations, and investigations the impact of different variables like a year, month, and weapon on the perplexing violations. The forecast stage assesses the portrayal of the culprits like, their age, sex, and relationship with the person in question. These expectations are done in view of the proof gathered from the crime location. The framework predicts the depiction of the culprit utilizing calculations like Multilinear Relapse, K-Neighbors Classifier, and Brain Organizations. It was prepared and tried utilizing the San Francisco Manslaughter dataset (1981-2014) and executed utilizing python.

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Cite This Article

ISSN: 2349-6002
Volume: 5
Issue: 4
PageNo: 401-404

Crime Data Analysis and Prediction of Suspect identity using Machine Learning Approach

Available:https://ijirt.org/Article?manuscript=157809

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Impact Factor
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UGC Approved
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