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ABOUT CHALLENGE

Natural calamities have always challenged mankind. They have caused incalculable damage to structures and properties and caused the deaths of millions of people throughout the world. Earthquake is a good description of a natural phenomenon which suddenly strikes an area causing damage that varies according to the intensity of the quake and local geological conditions.

When a damaging earthquake hits a populated area, it is very important to grasp the overall distribution of building damage within several hours to a few days of the event, for the purpose of emergency response and restoration planning. An automated model that can predict the extent of damage that is done to a building post earthquake can readily reduce the manpower required in analysing such tasks and lead to quick decision making.

This challenge encourages you to apply your machine learning skills to build models that can assess the degree of damage done to a building post an earthquake.

Why should you participate?

  • To learn and use the latest open-source libraries and packages
  • To learn by working on live problems because it excites you more than learning from books and tutorials!
  • To build your fan following in our community
  • Of course, grab cash prizes

Who should participate?

  • Working professionals
  • Data Science/Machine Learning enthusiasts
  • College students (if you understand basics of predictive modelling)

Tutorials

Notes

  • In order to be able to claim your prizes, your HackerEarth profile must be more than 50% complete.
  • The prizes will be disbursed before the end of December.

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PRIZES

There are great prizes to be won

1st Prize

USD 700 

2nd Prize

USD 500 

GUIDELINES

  1. When the contest is running, your output will be evaluated only for 40% of the test data. After the contest is over, your output for the remaining 60% of the test data w...
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