Train Test Split

import pandas as pd
df = pd.read_csv("train.csv")

df.sample(5)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
363	364	0	3	Asim, Mr. Adola	male	35.0	0	0	SOTON/O.Q. 3101310	7.050	NaN	S
508	509	0	3	Olsen, Mr. Henry Margido	male	28.0	0	0	C 4001	22.525	NaN	S
723	724	0	2	Hodges, Mr. Henry Price	male	50.0	0	0	250643	13.000	NaN	S
11	12	1	1	Bonnell, Miss. Elizabeth	female	58.0	0	0	113783	26.550	C103	S
183	184	1	2	Becker, Master. Richard F	male	1.0	2	1	230136	39.000	F4	S

corr = df.corr() # correlation
corr.Survived.sort_values(ascending=False)

Survived       1.000000
Fare           0.257307
Parch          0.081629
PassengerId   -0.005007
SibSp         -0.035322
Age           -0.077221
Pclass        -0.338481
Name: Survived, dtype: float64

y = df['Survived']

x = df.drop(['Survived', 'PassengerId'], axis=1) # drop column

Split

sklearn.model selection의 train_test_split

• train_test_split(X, y, test_size, shuffle…) (함수에 대해 자세히 알아보려면 shift+tab 키를 입력)
  • test_size: 전체 데이터 중 test_size 만큼 test data로 할당 (예를 들어, test_size = 0.3이면, 전체 데이터의 30%는 x_test와 y_test에 할당, 나머지는 x_train, y_train에 할당)

from sklearn.model_selection import train_test_split

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)