Identificación de zonas de riesgo para la Seguridad Vial mediante algoritmos de aprendizaje no supervisado

The following work applies Machine Learning algorithms as a tool for a possible solution to the problem of citizen security in a South American city. This application aims to reduce the threat risk to the physical integrity of pedestrians through the geolocation, in real time, using safer places to walk. A database of free disposal for the user is the Open Data San Isidro, district of Lima, Peru, which has been used in the development of this work. This database keeps records of different accidents types (most of the automobile type) occurring in different places of this district, this data will be used to determine safe areas in the route from one place to another, decreasing the probability of suffering an
accident. For this work, techniques of non-supervised learning algorithms of Clustering type: k-Means have been used. Likewise, a reduction of dimensions has previously been made using the Principal Component Analysis (PCA) technique.