titanic

Data 이해

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings

warnings.filterwarnings('ignore')

train = pd.read_csv('_data/train.csv')
test = pd.read_csv('_data/test.csv')

train.describe()

	PassengerId	Survived	Pclass	Age	SibSp	Parch	Fare
count	891.000000	891.000000	891.000000	714.000000	891.000000	891.000000	891.000000
mean	446.000000	0.383838	2.308642	29.699118	0.523008	0.381594	32.204208
std	257.353842	0.486592	0.836071	14.526497	1.102743	0.806057	49.693429
min	1.000000	0.000000	1.000000	0.420000	0.000000	0.000000	0.000000
25%	223.500000	0.000000	2.000000	20.125000	0.000000	0.000000	7.910400
50%	446.000000	0.000000	3.000000	28.000000	0.000000	0.000000	14.454200
75%	668.500000	1.000000	3.000000	38.000000	1.000000	0.000000	31.000000
max	891.000000	1.000000	3.000000	80.000000	8.000000	6.000000	512.329200

• Age 결측치 177개

test.describe()

	PassengerId	Pclass	Age	SibSp	Parch	Fare
count	418.000000	418.000000	332.000000	418.000000	418.000000	417.000000
mean	1100.500000	2.265550	30.272590	0.447368	0.392344	35.627188
std	120.810458	0.841838	14.181209	0.896760	0.981429	55.907576
min	892.000000	1.000000	0.170000	0.000000	0.000000	0.000000
25%	996.250000	1.000000	21.000000	0.000000	0.000000	7.895800
50%	1100.500000	3.000000	27.000000	0.000000	0.000000	14.454200
75%	1204.750000	3.000000	39.000000	1.000000	0.000000	31.500000
max	1309.000000	3.000000	76.000000	8.000000	9.000000	512.329200

• Age 결측치 86개
• Fare 결측치 1개: 이 정도는 그냥 삭제해도 될듯

plt.figure(figsize=(15, 10))

# Age 분포 확인
plt.subplot(2, 2, 1)
sns.boxplot(x='Survived', y='Age', data=train)
plt.title('Age 분포 (생존 여부별)')

# Fare 분포 확인
plt.subplot(2, 2, 2)
sns.boxplot(x='Survived', y='Fare', data=train)
plt.title('Fare 분포 (생존 여부별)')

# Pclass에 따른 Age 분포
plt.subplot(2, 2, 3)
sns.boxplot(x='Pclass', y='Age', data=train)
plt.title('Age 분포 (객실 등급별)')

# Pclass에 따른 Fare 분포
plt.subplot(2, 2, 4)
sns.boxplot(x='Pclass', y='Fare', data=train)
plt.title('Fare 분포 (객실 등급별)')

plt.tight_layout()
plt.show()

train_x = train.drop('Survived', axis=1).values
train_y = train['Survived'].values
test_x = test.values