Kaggle competition: Tabular Playground Series - Jan 2021
<SOS> metric: RMSE Kaggle data source: tabular-playground-series-jan-2021<train_model>
!pip install -q -U git+https://github.com/mljar/mljar-supervised.git@master
!pip install -q -U matplotlib==3.1.3
sparse.save_npz('train_x.npz', train_x)
 sparse.save_npz('test_x.npz', test_x)
<import_modules>
import pandas as pd
from supervised import AutoML
stemmer = PorterStemmer() 
<load_from_csv>
train_data = pd.read_csv('.. /input/tabular-playground-series-jan-2021/train.csv')
 test_data = pd.read_csv('.. /input/tabular-playground-series-jan-2021/test.csv')
 x_cols = [f"cont{i}" for i in range(1,15)]
train = pd.read_csv("/kaggle/input/home-depot-product-search-relevance/train.csv.zip", encoding="ISO-8859-1")
 test = pd.read_csv("/kaggle/input/home-depot-product-search-relevance/test.csv.zip", encoding="ISO-8859-1")
 attr = pd.read_csv('/kaggle/input/home-depot-product-search-relevance/attributes.csv.zip')
 desc = pd.read_csv('/kaggle/input/home-depot-product-search-relevance/product_descriptions.csv.zip' )<count_unique_values>
automl = AutoML(mode="Explain")
 automl.fit(train_data[x_cols], train_data["target"] )
display(train.head(10))
print('Số lượng product_uid khác nhau trong tập train:', train.product_uid.nunique() )<count_unique_values>
automl = AutoML(mode="Compete", total_time_limit=4*3600)
 automl.fit(train_data[x_cols], train_data["target"] )
<count_unique_values><EOS> 
preds = automl.predict(test_data)
 sub = pd.DataFrame({"id":test_data.id, "target":preds})
 sub.to_csv('submission.csv', index=False )