Performance Analysis for Crime Prediction and Detection Using Machine Learning Algorithms
Keywords:
Crime prediction, Machine Learning, Decision Tree, Linear Regression, detectionAbstract
Crime prediction system is essential to identifying and analyzing patterns and trends in crime. In recent era, the main aim of government is to reduce the crime events in all countries. Nowadays a greater number of crimes are perpetrated which affects the typical human’s life. Hence, crime prediction is a vital task. Various factors such as criminal behavior, age, place etc. are used to predict the crime pattern. The preeminent objective of this work is to identify the crime groups by using different years of dataset. Machine learning techniques are used to analysis and discover the crime patterns. In this work, a comparative analysis of various techniques like the Decision Tree, Gaussian Naïve Bayes, Linear Support Vector Classifier, Logistic Regression, and Stochastic Gradient Descent are used to suggest the crime pattern mainly based on time and place. Also, various existing feature selection techniques like Linear Regression, Ridge Regression and Polynomial Regression are used to select the salient features from the dataset. The performance of each technique is validated with various performance metrics such as accuracy, precision, recall and f1 score. The Experimental results show that the Linear Regression with Stochastic Gradient Descent model performing better than other techniques.
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