to prove the efficiency and accuracy of ML algorithms in predicting violent crime patterns and other applications, such as determining criminal hotspots, creating criminal profiles, and learning criminal trends.

Random forest is most accurate algorithm providing maximum accuracy of 90.23% in predicting the crime rate based on different factors like crime date, location, time, crime type. domestic, ward, description.

Random forest, logistic regression, and LightGBM are three well-known classification methods that can be applied to crime prediction. Random forest is an ensemble learning algorithm that predicts by combining multiple decision trees.

IV. ALGORITHMS RANDOM FOREST: A random forest produces good predictions that can be understood easily. It can handle large datasets efficiently. The random forest algorithm provides a higher level of accuracy in predicting outcomes over the decision tree algorithm. KNN :

(Khatun et al., 2021) gave an idea of how crime investigation agencies can utilize data mining to discover relevant precautionary measures from prediction rates using some supervised...

Jun 28, 2022 · Therefore, this paper proposes machine learning algorithm to indicate the frequency and pattern of crimes based on the data collected and to show the extent of crime in a particular region. Various visualization approaches and machine learning algorithms are used in this study to anticipate the crime distribution over a large area.

Mar 25, 2025 · Crime Rate Prediction using Random Forest, KNN, and K-Means Clustering. This project uses machine learning algorithms to predict crime rates and visualize crime hotspots.

it is necessary to understand the crime patterns. This study imposes one such crime pattern analysis by using crime data obtained from Kaggle open source which in turn used fo. the prediction of most recently occurring crimes. The major aspect of this project is to estimate which type of crime contributes the most along w.

algorithms such as decision trees, random forests, and neural networks are used in this study to build and compare predictive models. The findings reveal that the predictive model based on the random forests algorithm delivers the highest accuracy for predicting crime rates.

e. Random forests - Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks, that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of …