India generally represents pretty much one percent of the worldwide vehicle populace. Be that as it may, it represented around six per cent of the absolute worldwide street mishaps. A sum of 4,37,396 street mishap cases was accounted for during 2019.We intend to fabricate an expectation model, which will be prepared on the state-wise dataset, for various classifications like age gathering of the driver, climate conditions, street conditions and vehicular imperfections, the season of movement. In light of this prepared model, when a person enters their course of movement, age of the driver, state of the vehicle and season of movement, the model will anticipate the likelihood of mishap and what could be the reasonable justification of mishaps. We are gathering distinctive state-wise datasets and will group more than 4 diverse datasets to accomplish an adaptable and huge dataset with different contributing components. We intend to lead exploratory information examination after the information cleaning stage. This will assist us with acquiring bits of knowledge about major contributing components for each state. Then based on the correlation of different parameters a prediction model will be chosen. Using a Client Interface, information will be collected. The prediction model will run on a server and output will be shared on the Client Interface.
From the above state analysis the major causes of road accidents can be found to be Heavy rain, Very cold, Defective brakes, Sharp curves, Steep inclines. The future works for this project mainly include the improvement of the model. If the latest data is found after the year 2017, the accuracy of the model is expected to increase. Also, different supervised learning models can be tried out to possibly achieve more accuracy. The webapp for prediction is hosted and free to use: https://ritikagrawal06.shinyapps.io/RoadAccidentVisualizationShinyApp/ Other details and visualizations are available at: https://sites.google.com/view/road-accident-prediction/home