AN APPLICATION OF MACHINE LEARNING FOR ANALYSIS OF ROADWAY ACCIDENTS USING FEATURE CLASSIFICATION

Abstract

Roadway traffic wellbeing is a significant worry for transportation overseeing organizations just as standard citizens. Data Mining is removing from concealed examples from colossal database. It is ordinarily utilized in an advertising, reconnaissance, extortion location and logical revelation. In data mining, AI is essentially engaged as exploration which is naturally figured out how to perceive complex examples and settle on smart choices dependent on data. Globalization has influenced numerous nations. There has been an intense expansion in the monetary exercises and utilization level, prompting development of movement and transportation. The increment in the vehicles, traffic lead to street accidents. Thinking about the significance of the street wellbeing, government is attempting to distinguish the reasons for street accidents to lessen the accidents level. The dramatic expansion in the accidents data is making it hard to break down the limitations causing the street accidents. The paper depicts how to mine successive examples causing street accidents from gathered data set. We discover relationship among street accidents and anticipate the kind of accidents for existing just as for new streets. We utilize affiliation and characterization rules to find the examples between street accidents and just as foresee street accidents for new streets.