This is a notebook about my first dive into H2O Flow platform. Housing pricing data from Kaggle is imported to predict the house price using GBM(Gradient boosting method).
As it is my first dive into H2O flow, I focus on the UI more than data manipulation, such as feature engineering, data preprocessing, etc.. I build the GBM model with the original dataset.
What I learnt:
- H2O flow can be opened within localhost. The UI is very simple, but straightforward.
- Train and test the model with a few clicks, which is convenient. Also, I believe it is the trend of data science in the future.
- The model training process is very efficient with decent accuracy.
Future work: Next, I will get into autoML using H2O.