Refactored titanic code with pipelines

titanic-pipeline.py

import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from category_encoders import OneHotEncoder

# lendo o dataset
df = pd.read_csv("train.csv")

# retirando colunas com nome, ingresso e cabine dos conjuntos
df.drop(["Name", "Ticket", "Cabin"], axis=1, inplace=True)

# dividindo em conjunto de treino e test
X_train, X_test, y_train, y_test = train_test_split(df.drop(['Survived'], axis=1), 
                                                    df['Survived'], 
                                                    test_size=0.2, 
                                                    random_state=42)

# criando o modelo usando pipeline
model = Pipeline(steps=[
    ('one-hot encoder', OneHotEncoder()),
    ('imputer', SimpleImputer(strategy='mean')),
    ('tree', DecisionTreeClassifier(max_depth=3, random_state=0))
])

# treinando o modelo
model.fit(X_train, y_train)
train_score = model.score(X_train, y_train)

# avaliando o modelo
test_score = model.score(X_test, y_test)

print("Train score: {}".format(train_score))
print("Test score: {}".format(test_score))
# OUTPUT
# Train score: 0.8342696629213483
# Test score: 0.7988826815642458