Road crashes are a significant global problem, resulting in the loss of over 1.19 million lives and causing around 50 million severe injuries each year. However, despite the efforts made by the Government to improve the situation, no significant results have been observed in respect to decline in number of accidents and deaths in India. Road safety is a complex issue and requires a multifaceted approach including adequate research and development in the field. At present the mostly traditional approach is applied in terms of data collection, statistical analysis and decision making. Over time, the Government may start building advanced data analytics solutions for prediction of various road crash attributes. With this intent this paper aims to depict the application of combination of different algorithms including Support Vector Machine, Random Forest, Decision Tree, Artificial Neural Network, K Nearest Neighbor, Logistic Regression, and Gradient Boosting Algorithm for prediction of road crash attributes.

The absence of public safety normalization bodies can antagonistically affect the reception of worldwide security guidelines and best practices. This research is done for the benefit of people, process and technology for any organization. This paper presents a novel pragmatic network protection evaluation structure that is custom-made to the ISO 2700x standard necessities for the improvement of Information Security Management System (ISMS). This model can be utilized for both self-appraisal and examining/scoring instruments by public network protection specialists. Utilizing this model, associations can consider their current data security the broad frameworks in contrast to nearby and global guidelines by using worked in pre-review instruments. All things considered, the model will assist associations with assessing and improving their status for developing dangers and dangers. In this system, an original numerical model was likewise planned and carried out for the scoring/rating instrument, specifically, the public digital protection list (aeNCI). The aeNCI utilizes various boundaries to decide the development of existing network safety programs at public associations and produce a classification and correlation reports. The outcomes empowered the partner to confirm the security configuration of their frameworks and recognize possible attacks/hazard vectors.