Development Of A Predictive Model Of Automobile Accidents In The City Of Bogotá, Colombia. - H., Fredys A. Simanca; Blanco Garrido, Fabian; Barbosa Guerrero, Lugo Manuel; Abuchar Porras, Alexandra; Rozo, Jairo Jamith Palacios (Journal of Namibian Studies)
The importance of information analysis has allowed great advances in recent years, including predictive analysis, which, based on a series of data, look for patterns to forecast unknown data. For this article, one of the algorithms for predictions within Machine Learning is used, such as Random Forest. And, the algorithm is made in the Python programming language, in order to obtain the possible causes of accidents in the city of Bogotá. This information is taken from the open data of accidents registered in the city in order to know how many accidents will exist each day and what will be the causes to be analyzed and thus, find a concrete solution to these problems of accident rate with the purpose of reducing them considerably. In conclusion, results were obtained from the development of the algorithm that allow to predict with a certain degree of accuracy the automobile accident rate in the city.