Predicting Crime rate in Punjab: An Analysis of Artificial Intelligence based Models

Authors

  • Bisma Ali Lahore Grammar School, 55 Main Gulberg Lahore, Pakistan.

DOI:

https://doi.org/10.62019/abbdm.v4i1.76

Abstract

This study aims at measuring and evaluating the accuracy rate or Mean Square Error (MSE) of different machine learning models to predict crime count (dependent variable) on the basis of a number of independent variables such as shift, day, month, police station, district and time of the day. It is a quantitative research study where primary data is collected from rescue 15 call record. The year 2022 has been chosen for the purpose of data under the crime head of theft across the Punjab province which is divided into three eight hourly shifts each in a day during any week of a month. The crime count indicates number of incidents of theft reported during these three shifts per day per month in a district in 2022. The data containing variables is then used for prediction through an appropriate machine learning model. The MSE value of Random Forest Regression (RFR) is greater than MSE value of Multi-Layer Perceptron Regression (MLPR). It is however lower than MSE score of Linear Regression (LR) model and greater than MSE value of Support Vector Regression (SVR). This shows that MLPR model is the most suitable one with least error as compared to other models which have higher MSE values while predicting the dependent variable (theft crime count). The study findings may assist the intelligence branches of police to efficiently utilize their material and human resources while applying the appropriate model of machine learning to predict the number of crime incidents in any geographical dispensation at sub national level.

Author Biography

Bisma Ali, Lahore Grammar School, 55 Main Gulberg Lahore, Pakistan.

She is a young researcher  who is interested in artificial intelligence and other related fields.

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Published

2024-03-28

How to Cite

Ali, B. (2024). Predicting Crime rate in Punjab: An Analysis of Artificial Intelligence based Models. The Asian Bulletin of Big Data Management, 4(1). https://doi.org/10.62019/abbdm.v4i1.76