Hello World! We’re a group of three friends from Hacettepe University Computer Engineering department and we will be sharing our semester-long Machine Learning project’s weekly updates here. Let’s start with the introduction.
Earthquake is one of the most deadly disasters and people are unaware of it. Most people still living in flimsy, old buildings and it is almost impossible to survive from an earthquake while living in such kinds of buildings. So far adequate measures have not been taken by humanity. Of course, there are many studies to reduce the earthquake’s effect on people. Most of them aim to predict the disaster before happening quite some time so that people can survive off it. However, even this day, there isn’t enough successful study to predict the earthquake. What if we would take measures to face an earthquake instead of trying to predict it?
Our goal in this project is, to predict a possible future earthquake’s effects on the buildings using a dataset collected from a past massive earthquake. By using this information, in case of a similar future earthquake, possible damage level of a specific house can be predicted and adequate measures can be taken before any earthquake happens. We will be trying to classify each building on a damage level scale of 1–5.
Dataset
In 2015, a massive earthquake happened in Nepal and approximately 10,000 people died and millions of people injured just in a couple of hours. After this disaster, Nepal’s National Planning commission collected a huge dataset with surveying earthquake victims and it contains a variety of building information such as floor type, structural type, building age, etc.
Contributors
To be continued…
