In this project, data was collected from querying a SQL database. Data was explored and cleaned to build a classification model to predict building damage for the district of Kavrepalanchok. • Wrote a wrangle function that create a “severe_damage” column, where all buildings with a damage grade greater than 3 should be encoded as 1. All other buildings should be encoded at 0, drop any columns that could cause issues with leakage or multicollinearity in your model.

• Used seaborn to create a boxplot that shows relationship between the footprint size of a building and the damage it sustained in the earthquake
• Created a logistic regression model to predict building damage using an appropriate encoder for categorical features.
• Built a decision tree model that perform better than logistic regression.

Link to GitHub Repository