Kaggle competition: House Prices - Advanced Regression Techniques
<SOS> metric: rmsle Kaggle data source: house-prices-advanced-regression-techniques<categorify>
sns.set_theme()
sns.set_palette('muted')
 h2o.init()
train_ds = train_ds.map(
 input_preprocess, num_parallel_calls=tf.data.experimental.AUTOTUNE 
)
 val_ds = val_ds.map(input_preprocess )<choose_model_class>
df = pd.read_csv('.. /input/house-prices-advanced-regression-techniques/train.csv')
 df
strategy = tf.distribute.MirroredStrategy()<train_model>
hf = h2o.H2OFrame(df)
 x = list(df.columns.difference(['Id', 'SalePrice'] ).values)
 y = "SalePrice"
aml = H2OAutoML(max_models=100, seed=1)
 aml.train(x=x, y=y, training_frame=hf)
 lb = aml.leaderboard
lb.head(rows=lb.nrows )
<prepare_output><EOS> 
df_test = pd.read_csv('.. /input/house-prices-advanced-regression-techniques/test.csv')
 hf_test = h2o.H2OFrame(df_test)
 x = list(df.columns.difference(['Id'] ).values)
 preds = aml.predict(hf_test);
h2o.h2o.download_csv(preds, 'h20_output.csv')
 df_SalePrice = pd.read_csv('./h20_output.csv')
 output_df = pd.DataFrame({
'Id': df_test['Id'],
'SalePrice': df_SalePrice['predict']
})
 output_df.to_csv('output.csv', index=False )