Model predictive contrTraffic Collisions are one of the major sources of deaths, injuries & property damage every year. Road accidents are one of the most difficult real world problems to tackle with, due to its high order of unpredictability. The persistence as well as existence of this problem may be prevalent to a different degree for each & every place. The consequences of this may result in loss of human life & capital. To avoid this, every place needs to tackle the problem with a customized approach depending on the causes that are responsible for the accidents. Even in today's world, where the mass operation of autonomous vehicles is still grim or out of sight, the possibility of predicting a road accident before it takes place, is practically impossible. The only idea or approach that can help to decrease the number of road accidents, is to analyze the reasons that lead to these accidents. The concepts of Data Analysis, Data Visualization & Machine Learning help to tackle real world problems, by exploring & deriving valuable insights, which in turn help in taking measures to solve the targeted problem & drive business growth. In this research study, the dataset pertaining to road mishaps that occurred in UK over time period 2005 - 2015 will be analyzed using these concepts. The defined approach can help the concerned authorities & respective government, to take every possible step & amendment, & hence mitigate the identified causes & scenarios that lead to road accidents.