Crime Rate Prediction Indian Cities Using Random Forest Classifiers

Abstract

Crime Rate Prediction Is Essential For Proactive Law Enforcement And Urban Safety Management. This Study Utilizes A Random Forest Classifier To Predict Crime Rates Across Major Indian Cities, Aiming To Identify Trends And High-risk Areas. The Random Forest Algorithm, Known For Its Robustness In Handling Complex, Non-linear Data, Was Trained On Historical Crime Data, Including Factors Like Demographics, Economic Indicators, And Previous Crime Statistics. Results Demonstrate That The Model Achieves Significant Accuracy, Highlighting Key Predictors And Enabling Better Decision-making For Authorities. This Approach Provides A Foundation For Scalable, Data-driven Crime Prevention Strategies Across Diverse Urban Environments In India. Crime Rates In Indian Cities Have Increased Significantly, Posing Threats To Public Safety And Social Stability. Effective Crime Prediction And Prevention Strategies Are Crucial To Address This Issue. This Study Aims To Develop A Predictive Model Using Random Forest Classifiers To Forecast Crime Rates In Indian Cities.

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