Predicting the nature of fire based on machine learning: Logistic regression is an interpretable algorithm

One of the duties of firefighting organizations is to submit fire and accident reports to judicial authorities, insurance, and other requesting institutions to make decisions and pay damages, therefore, finding the nature of the fire in such a way that non-operational components have the least impact on the decision of firefighting experts increases the importance of this research. Considering that about 1% of the fire reports of this organization are unknown, this issue has confused providing proper services to Arbab-Rojoo and decision-making has been difficult. This research aims to predict the nature of fire based on machine learning algorithms in the city of Mashhad. In this research, the 7-year fire data set (1395-1401) was first examined and analyzed, and then, according to the problem and the literature, a data set with 46 features and 28930 samples was prepared by pre-processing and feature engineering. In the next step, to predict the nature of the fire, three machine learning algorithms were used with the observer and their results were compared. The logistic regression algorithm, with 79.66% accuracy and an execution time of 1 second, created a better result between the three algorithms in predicting the nature of the fire.