가설

3등석에 탑승한 사람들이 더 많이 살아남았을것 같다!

(1등석이 살아남은 비율은 높을 수 있으나 3등석 승객이 더 많기 때문)

코드

# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: <https://github.com/kaggle/docker-python>
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session

titanic_train = pd.read_csv("/kaggle/input/titanic/train.csv") // 데이터 저장
titanic_test = pd.read_csv("/kaggle/input/titanic/test.csv")

titanic_train.isnull().sum() // 결측치 없음 확인

sns.barplot(x='Pclass',y='Survived',data = titanic_train) // 생존률 시각화 (1등석의 생존률이 압도적으로 높음)

titanic = titanic_train.groupby(['Pclass','Survived'])['Survived'].count()
titanic.plot(kind = "bar") // 생존자, 사망자 수 시각화 (1등석 생존자가 근소하게 더 많음)

결과

1등석의 생존률이 높을 줄은 알았지만 3등석의 생존자 수를 찍어누를줄은 몰랐다..

돈이 최고다