Predictive Analysis and Forecasting of Crime Patterns in Jharkhand Using Linear Regression and Random Forest Models

Anubhuti Srivastava

Predictive Analysis and Forecasting of Crime Patterns in Jharkhand Using Linear Regression and Random Forest Models

Author(s)	Anubhuti Srivastava
Country	India
Abstract	Crime prediction plays an important role in understanding crime trends and supporting decision-making in law enforcement. This paper presents a predictive analysis of crime patterns in Jharkhand using district-wise crime records collected during 2024 and 2025. The dataset contains information on several categories of cognizable offences, including murder, dacoity, robbery, burglary, theft, riot, kidnapping, rape, Arms Act cases, Naxal-related incidents, and miscellaneous crimes. After data pre-processing and feature selection, Linear Regression and Random Forest Regression models were developed to predict Total Cognizable Crime. The performance of the models was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination R^2. Experimental results showed that the Linear Regression model significantly outperformed the Random Forest Regression model. Linear Regression achieved an RMSE of 4.37 and a R^2 value of 0.999993, whereas Random Forest Regression produced an RMSE of 553.38 and a R^2 value of 0.894390. The results indicate a strong linear relationship between the selected crime variables and Total Cognizable Crime. The results showed that Linear Regression provided more accurate predictions than Random Forest Regression for the available dataset. The findings demonstrate that historical crime records can be effectively used for crime forecasting and pattern analysis. The proposed approach offers a simple and reliable framework for predicting crime trends and highlights the potential of machine learning techniques for crime analytics in Jharkhand.
Keywords	Crime Prediction, Crime Forecasting, Machine Learning, Linear Regression, Random Forest Regression, Jharkhand.
Field	Engineering
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-07-03

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CrossRef DOI prefix of IJFMR is 10.36948/ijfmr

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Predictive Analysis and Forecasting of Crime Patterns in Jharkhand Using Linear Regression and Random Forest Models

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