The occurrence of road accidents continues to be one of the prominent causes of deaths, disabilities and hospitalisation in the country. This makes traffic accident risk prediction important in order to minimise it and save lives. Several kinds of models have been proposed to achieve the same ranging from old statistical models to the new models motivated by the advent of machine learning. This paper presents a comparative study of a variety of these models in an effort to analyse and deduce a beneficial approach to traffic accident risk prediction. Since the drivers are the ones in control on the road the study aims to provide traffic accident risk prediction to the drivers by analysing the factors they would know of beforehand like vehicle type, age sex, time of the day and weather etc. Optimal Classification Trees is a model that would provide such results that make intuitive sense to the driver along with the use of Random Forest and Logistic Regression. Furthermore, the geolocation data analysis using K-means clustering algorithm can provide information regarding places that are more prone to accidents. Through the analysis of previously known factors using these algorithms the drivers can be equipped with traffic accident risk predictions that would help them make informed decisions to minimise the sames.