code.py

import pandas as pd
from sklearn.preprocessing import OneHotEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.compose import make_column_transformer
from sklearn.pipeline import make_pipeline

cols = ['Parch', 'Fare', 'Embarked', 'Sex']

df = pd.read_csv('http://bit.ly/kaggletrain', nrows=10)
X = df[cols]
y = df['Survived']

df_new = pd.read_csv('http://bit.ly/kaggletest', nrows=10)
X_new = df_new[cols]

ohe = OneHotEncoder()

ct = make_column_transformer(
    (ohe, ['Embarked', 'Sex']),
    remainder='passthrough')

logreg = LogisticRegression(solver='liblinear', random_state=1)

pipe = make_pipeline(ct, logreg)
pipe.fit(X, y)
pipe.predict(X_new)