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.

Based on post earthquake data we need to predict the degree of damage in the buildings.Using the machine learning model, the degree of damage needs to be predicted in three levels i.e. High, Medium, Low.Used Central Imputation for missing values, Feature selection done by using correlation plot and random forest.