The overall scope of this project is to predict the level of damages of buildings in Nepal during an earthquake using different features of individual buildings. In the data exploratory phase, the team implemented ordinal regression and aim to prevent over/under-fitting by splitting their data into training and testing set. They plan to further implement k-fold validation and regularization model.
Like correlation maps, allowed the audience to see the correlation between variables and what variables are used within models
Went in dept about some of the decisions made during data cleaning
Indicated findings in each visualization and what it signifies for the data
Areas of Improvement:
Going in-depth into why you choose to use certain models and what are the advantages
give more background context/variable definition
Connect how your visualizations help you build your model
The overall scope of this project is to predict the level of damages of buildings in Nepal during an earthquake using different features of individual buildings. In the data exploratory phase, the team implemented ordinal regression and aim to prevent over/under-fitting by splitting their data into training and testing set. They plan to further implement k-fold validation and regularization model.
Areas of Improvement: