I have a dataset related to work-safety that covers over 100,000 worksites over a course of 15 years. I would like to experiment with machine learning models to predict the number of injuries that will occur the next year. The objective is to create a tool that takes as input an identifier for the site and returns a prediction for the number of injuries in the upcoming year. My dataset contains indicator variables, continuous variables and categorical variables. Can you suggest models that would best leverage this data. The correlation coefficient between the average number of injuries by site and the number that occurred in the previous year on record is 0.70. How can I have my model make use of this information?
Indicator variables include whether specific types of safety gear and protocols are required, if the site is remote, the presence of a health and safety committee, the presence of hazardous substances. Categorical variables include the NAICS code, geographical region, the severity rating of the site (low, medium, high). The continuous variables include the total number of injuries, explosions, the number of assignments/inspectors that visited the site in previous year, the number of health and safety violations, the number of people employed at the site, the number of training sessions and corrective assignments charged to the site. The data is highly variable. The levels of risk vary with the line of work. Some sites require no safety material, have no violations and report no injuries. Other sites report hundreds of injuries. Given this, I would like to be able to use this data to predict the number of injuries that will occur the upcoming year.