Building Model for Crime Pattern Analysis Through Machine Learning Using Predictive Analytics (original) (raw)
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Complexity, 2022
The crime is difficult to predict; it is random and possibly can occur anywhere at any time, which is a challenging issue for any society. The study proposes a crime prediction model by analyzing and comparing three known prediction classification algorithms: Naive Bayes, Random Forest, and Gradient Boosting Decision Tree. The model analyzes the top ten crimes to make predictions about different categories, which account for 97% of the incidents. These two significant crime classes, that is, violent and nonviolent, are created by merging multiple smaller classes of crimes. Exploratory data analysis (EDA) is performed to identify the patterns and understand the trends of crimes using a crime dataset. The accuracies of Naive Bayes, Random Forest, and Gradient Boosting Decision Tree techniques are 65.82%, 63.43%, and 98.5%, respectively, and the proposed model is further evaluated for precision and recall matrices. The results show that the Gradient Boosting Decision Tree prediction mo...
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USING MACHINE LEARNING ALGORITHMS TO ANALYZE CRIME DATA
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An Experimental Study of Classification Algorithms for Crime Prediction
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Machine Learning Algorithms for Visualization and Prediction Modeling of Boston Crime Data
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Machine learning plays a key role in present day crime detection, analysis and prediction. The goal of this work is to propose methods for predicting crimes classified into different categories of severity. We implemented visualization and analysis of crime data statistics in recent years in the city of Boston. We then carried out a comparative study between two supervised learning algorithms, which are decision tree and random forest based on the accuracy and processing time of the models to make predictions using geographical and temporal information provided by splitting the data into training and test sets. The result shows that random forest as expected gives a better result by 1.54% more accuracy in comparison to decision tree, although this comes at a cost of at least 4.37 times the time consumed in processing. The study opens doors to application of similar supervised methods in crime data analytics and other fields of data science
A Study on Classification Algorithms for Crime Records
Data mining has its popularity among crime data analysis significantly due to increasing crime rates across the globe. In this research, classification methods are applied for predicting the nature of a crime that is whether the crime is a violent crime or a non-violent crime. In this work, we present two classification algorithms-Gradient Boosting algorithm and Random Forest algorithm for predicting the crime as a violent or non-violent crime and analyze the accuracy, precision and recall values of these algorithms for the crime records. The dataset is taken from the Communities and Crime data from UCI repository for processing. Further, to improve the accuracy of the predicted results, we use Boruta algorithm which is primarily a wrapper-algorithm for all relevant feature selections. The study finds that Boruta algorithm performs better in feature selection than the Chi-Square feature selection algorithm.
IRJET- PREDICTION of CRIME RATE ANALYSIS using MACHINE LEARNING APPROACH
IRJET, 2020
In recent years, report points out that the crimes in India have seen a spike. The report adds that the cases of murder, rapes, and kidnapping have seen a rise. Most of countries in the world have seen a remarkable increase in the crime rate. There is no particular reason for any trouble for criminal activities. To prevent this problem in police sectors have to predict crime rate using machine learning techniques. The aim is to investigate machine learning based techniques for crime rate by prediction results in best accuracy and explore in this work the applicability of data technique in the efforts of crime prediction with particular importance to the data set. The analysis of dataset by supervised machine learning technique(SMLT) to capture several information's like, variable identification, uni-variate analysis, bi-variate and multi-variate analysis, missing value treatments and analyze the data validation, data cleaning/preparing and data visualization will be done on the entire given dataset. Our analysis provides a comprehensive guide to sensitivity analysis of model parameters with regard to performance in prediction of crime rate by accuracy calculation from comparing supervise classification machine learning algorithms.
Data mining technique to analyse and predict crime using crime categories and arrest records
Indonesian Journal of Electrical Engineering and Computer Science, 2021
Generally, crimes influence organisations as it starts occurring frequently in society. Because of having many dimensions of crime data, it is difficult to mine the available information using off the shelf or statistical data analysis tools. Improving this process will aid the police as well as crime protection agencies to solve the crime rate in a faster period. Also, criminals can often be identified based on crime data. Data mining includes strategies for the convergence of machine learning and database frameworks. Using this concept, we can extract previously unknown useful information and their patterns of occurrence from unstructured data. The sole purpose of this paper is to give an idea of how data mining can be utilised by crime investigation agencies to discover relevant precautionary measures from prediction rates. Data sets are analysed by some supervised classification algorithms, namely decision tree, K-nearest neighbours (KNN), and random forest algorithms. Crime forecasting is done for frequently occurring crimes like robbery, assault, and theft. Specifically, the results indicate the superiority of the random forest algorithm in test accuracy.
Crime Prediction is an approach for distinctive future crimes that are most seem to happen in an exceedingly particular location at a selected given timestamp. Daily there are a large variety of crimes committed frequently. By exploiting the knowledge set we'll understand the world of crime which can facilitate us in reducing the crime rate. The dataset is of Indore city. The dataset consists of crime data comparable to timestamp, style of crimes, latitude, and longitude. foremost we've done data preprocessing for removing the null values for obtaining high accuracy. We've done the testing and training using K-means clustering, Random forest, and Decision tree algorithms. We tend to have used these three algorithms for correct prediction and for obtaining the very best accuracy. The image of the dataset is completed in terms of graphical illustration comparable to feature selection, making the random forest tree of the complete dataset, employing a box plot for every style of crime. The only purpose of this project is to give a jest plan of how machine learning will be utilized by enforcement agencies to detect, predict and solve crimes at a way quicker rate and therefore reducing the crime rate.
IEEE Access
Ensemble learning method is a collaborative decision-making mechanism that implements to aggregate the predictions of learned classifiers in order to produce new instances. Early analysis has shown that the ensemble classifiers are more reliable than any single part classifier, both empirically and logically. While several ensemble methods are presented, it is still not an easy task to find an appropriate configuration for a particular dataset. Several prediction-based theories have been proposed to handle machine learning crime prediction problem in India. It becomes a challenging problem to identify the dynamic nature of crimes. Crime prediction is an attempt to reduce crime rate and deter criminal activities. This work proposes an efficient authentic method called assemble-stacking based crime prediction method (SBCPM) based on SVM algorithms for identifying the appropriate predictions of crime by implementing learning-based methods, using MATLAB. The SVM algorithm is applied to achieve domain-specific configurations compared with another machine learning model J48, SMO Naïve byes bagging and, the Random Forest. The result implies that a model of a performer does not generally work well. In certain cases, the ensemble model outperforms the others with the highest coefficient of correlation, which has the lowest average and absolute errors. The proposed method achieved 99.5% classification accuracy on the testing data. The model is found to produce more predictive effect than the previous researches taken as baselines, focusing solely on crime dataset based on violence. The results also proved that any empirical data on crime, is compatible with criminological theories. The proposed approach also found to be useful for predicting possible crime predictions. And suggest that the prediction accuracy of the stacking ensemble model is higher than that of the individual classifier.