A Road Accident Prediction Model Using Data Mining Techniques | Python Machine Learning project

A Road Accident Prediction Model Using Data Mining Techniques | Python Machine Learning Final Year IEEE Project.
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?Project Title: A Road Accident Prediction Model Using Data Mining Techniques.
?Implementation Code: Python.
?Algorithm / Model Used: Adaboost Classifier Algorithm.
?Web Framework: Flask.
?️Frontend: HTML, CSS, JavaScript.
?Cost (In Indian Rupees): Rs.3000/.

IEEE Base paper Abstract:
Due to the exponentially increasing number of vehicles on the road, the number of accidents occurring on a daily basis is also increasing at an alarming rate. With the high number of traffic incidents and deaths these days, the ability to forecast the number of traffic accidents over a given time is important for the transportation department to make scientific decisions. In this scenario, it will be good to analyze the occurrence of accidents so that this can be further used to help us in coming up with techniques to reduce them. Even though uncertainty is a characteristic trait of majority of the accidents, over a period of time, there is a level of regularity that is perceived on observing the accidents occurring in a particular area. This regularity can be made use of in making well informed predictions on accident occurrences in an area and developing accident prediction models. In this paper, we have studied the inter relationships between road accidents, condition of a road and the role of environmental factors in the occurrence of an accident. We have used Adaboost classifier.

REFERENCE:
Dhanya Viswanath, Preethi K, Nandini R, Bhuvaneshwari R, “A Road Accident Prediction Model Using Data Mining Techniques”, IEEE Conference, 2021.

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