• R. Sravan Kumar
• Dr.R.Radha

In transportation field, an enormous measure of information gathered by IOTframeworks, remote detecting and other information accumulation apparatuses brings new difficulties; the span of this information turns out to be amazingly huge and progressively complex for customary methods of information mining. To manage this test, Apache Spark remain as an amazing huge scale circulated processing stage that can be utilized effectively for machine learning against exceptionally expansive databases. This work utilized substantial scale machine learning strategies particularly Decision Tree with Apache Spark system for enormous information investigation to assemble a model that can foresee the elements lead to street mishaps dependent on a few info factors identified with auto collisions. In view of this, the anticipating model first preprocesses the huge mishap information and investigates it to make information for a learning framework. Observational outcomes demonstrate that the proposed model could give new data that can help the leaders to break down and enhance street wellbeing.

Deep learning, deep neural network, Internet of Things, on-device intelligence, IOT big data, fast data analytics, cloud-based analytics.

"Big Data Analysis Application for Road Accident with Decision Tree ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.83-86, April-2019, Available :http://www.jetir.org/papers/JETIRAZ06016.pdf

"Big Data Analysis Application for Road Accident with Decision Tree ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp83-86, April-2019, Available at : http://www.jetir.org/papers/JETIRAZ06016.pdf