Kaggle competition: Jigsaw Unintended Bias in Toxicity Classification
test = create_dummies(test, "Fare_categories")
 test.head()<feature_engineering>
submission_1 = pd.read_csv(".. /input/sub19042/submission_19042.csv")
 submission_2 = pd.read_csv(".. /input/submission-v3/submission.csv")
 submission_3 = pd.read_csv(".. /input/blend2/submission.csv")
 submission_4 = pd.read_csv(".. /input/blend3/submission.csv")
 submission_5 = pd.read_csv(".. /input/blend4/submission.csv" )
train["Cabin_type"] = train["Cabin"].str[0]
train["Cabin_type"] = train["Cabin_type"].fillna("Unknown")
 train["Cabin_type"].head()<feature_engineering>
submission = pd.DataFrame.from_dict({
'id': submission_1['id'],
'prediction':(submission_1.prediction.values * 0.5)+(submission_2.prediction.values * 0.3)+(submission_3.prediction.values * 0.125)+(submission_4.prediction.values * 0.0375)+(submission_5.prediction.values * 0.0375)
 } )
test["Cabin_type"] = test["Cabin"].str[0]
test["Cabin_type"] = test["Cabin_type"].fillna("Unknown")
 test["Cabin_type"].head()<define_variables>
submission.to_csv('submission.csv', index=False )
<categorify><EOS> 
submission.to_csv('submission.csv', index=False )