Determining the degree of damage that is done to buildings post an earthquake can help identify safe and unsafe buildings, thus avoiding death and injuries resulting from aftershocks. Leveraging the power of machine learning is one viable option that can potentially prevent massive loss of lives while simultaneously making rescue efforts easy and efficient.
In this challenge we provide you with the before and after details of nearly one million buildings after an earthquake. The damage to a building is categorized in five grades. Each grade depicts the extent of damage done to a building post an earthquake.
Given building details, your task is to build a model that can predict the extent of damage that has been done to a building after an earthquake.
Data can be downloaded from the kaggle. click here to download the data
- final_model -- Saved Model File
- PredictingBuildingDamage_Trees -- Project Source Code File
Notebook is based on the work done by Abhishekmamidi123