Considering Safest and Shortest Path using Crime Analysis

Abstract

Safe Paths is a mobile-based application in which users can find the safest and the shortest possible route. Safe Paths is a recommendation system that suggests users to use the safest and the shortest path where the crime ratio low as compared to other routes. A very large number of methods are used to provide the easiest and safest route like google maps and other applications. However, not all the applications consider the safest paths. The proposed project analyses information to enhance the security of the people. In the proposed application, we are using various techniques of Data Mining and Machine Learning (ML) to analyse crime data and suggest the safest path. The application is developed with the help of Android Java Native Framework and ML Libraries. We have applied different ML techniques such as Linear Regression, K Nearest Neighbour (KNN), Decision Tree, Naive Bayes and, Random Forest algorithm on the dataset. Among all the algorithms, KNN has achieved the maximum accuracy of 92%. The proposed application provides an efficient time-saving solution and suggest the best route by predicting the crime rate for the safest and shortest path.