Crime Analytics using Machine Learning
Prof. Suman Acharya, Assistant Professor, University of Engineering and Management, Jaipur (Rajasthan), India.
Manuscript received on 07 January 2023 | Revised Manuscript received on 14 March 2023 | Manuscript Accepted on 15 March 2023 | Manuscript published on 30 March 2023 | PP: 1-5 | Volume-10 Issue-3, March 2023 | Retrieval Number: 100.1/ijies.F74440311623 | DOI: 10.35940/ijies.F7444.0310323
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Crime is one of the most significant and pervasive problems in our society, and preventing it is a crucial duty. A large number of crimes are perpetrated each day. Maintaining and analyzing crime data to forecast and solve crimes is the current issue. This project analyzes a large dataset of crimes and predicts future crimes based on conditions. This project uses data science and machine learning for India’s crime data prediction. Thus, Decision Tree, Logistic Regression, Multi-Regression, k-NN, Lasso & Ridge, and Random Forest are all involved in the supervised classification problem. Predicting crimes and classifying effective pattern detection and visualization equipment Utilizing crime data trends from the past allows us to correlate aspects that may help us comprehend the breadth of crimes in the future. This study uses visualization and machine learning methods to estimate future crime rates. First, raw datasets were processed and displayed.
Keywords: Predictive Analytics, Crime Analytics, Machine Learning, Performance Enhancement, Pattern detection, Decision Learning.
Scope of the Article: Machine Learning