Introduction
The amount of casualties, injuries, and deaths caused by traffic accidents each year, as well as the significant economic costs involved, make them one of the most prominent global concerns. There are many factors that are responsible for causing road accidents. If these factors can be better understood and predicted, it might be possible to take measures to mitigate the damages and their severity.
This case study is part of Microsoft Code Without Barriers Hackathon 2022 program.
POWER BI DATA VIsualisation
What it does?
Globally, Every year, approximately 1.3 million people's lives are cut short due to a road traffic crash (WHO,2021). The number of casualties, injuries, and deaths caused by traffic accidents each year and the significant economic costs involved make traffic accidents one of the most prominent global concerns. A variety of factors cause road accidents. If these factors can be better understood and predicted, it might be possible to take measures to mitigate the damages and their severity.
What did I learn?
The case study calls for further steps in identifying dynamic hotspots so that public and private entities can better manage resources.
The effective identification of dynamic hotspots can be used in several use cases, including allowing:
- local municipal / city authorities to more effectively organize their traffic control systems.
- Insurance companies to share this information with their clients for a safer driving experience.
- End-user apps such as Waze optimize routes by anticipating dangerous spots.
What challenges did I face?
This is my first time using Azure for machine learning.
To implement and publish the model, there is a steep learning curve. As I do not have access to Power BI, I must borrow a window device in order to conduct the analysis and visualisation. Nevertheless, Microsoft Azure and Power BI are both extremely powerful tools. I look forward to using Microsoft Azure and Power BI more in my professional capacity.