파이썬 스케일링 실습

스케일링 실습¶

In [ ]:

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

In [ ]:

# 타이타닉 데이터
titanic = pd.read_csv('C:/Users/LOVE/Downloads/vscode/ML/titanic/train.csv')

titanic.head(3)

Out[ ]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S

In [ ]:

titanic.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   PassengerId  891 non-null    int64  
 1   Survived     891 non-null    int64  
 2   Pclass       891 non-null    int64  
 3   Name         891 non-null    object 
 4   Sex          891 non-null    object 
 5   Age          714 non-null    float64
 6   SibSp        891 non-null    int64  
 7   Parch        891 non-null    int64  
 8   Ticket       891 non-null    object 
 9   Fare         891 non-null    float64
 10  Cabin        204 non-null    object 
 11  Embarked     889 non-null    object 
dtypes: float64(2), int64(5), object(5)
memory usage: 83.7+ KB

In [ ]:

sns.pairplot(titanic[['Age', 'Fare']])

Out[ ]:

<seaborn.axisgrid.PairGrid at 0x1fe46b89c40>

In [ ]:

titanic[['Age', 'Fare']].describe()

Out[ ]:

	Age	Fare
count	714.000000	891.000000
mean	29.699118	32.204208
std	14.526497	49.693429
min	0.420000	0.000000
25%	20.125000	7.910400
50%	28.000000	14.454200
75%	38.000000	31.000000
max	80.000000	512.329200

In [ ]:

from sklearn.preprocessing import MinMaxScaler, StandardScaler
mnmx = MinMaxScaler()
stnd = StandardScaler()

1. MinMaxScaler: 나이(Age)¶

In [ ]:

age_mean = titanic[['Age']].mean().round(2)
titanic['Age_mean'] = titanic['Age'].fillna(age_mean)

In [ ]:

titanic['Age_mean_mnmx'] = mnmx.fit_transform(titanic[['Age_mean']])
titanic.head(3)

Out[ ]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	Age_mean	Age_mean_mnmx
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S	22.0	0.271174
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C	38.0	0.472229
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S	26.0	0.321438

2. StandardScaler: 요금(Fare)¶

In [ ]:

titanic['Fare_stnd'] = stnd.fit_transform(titanic[['Fare']])
titanic.head(3)

Out[ ]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	Age_mean	Age_mean_mnmx	Fare_stnd
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S	22.0	0.271174	-0.502445
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C	38.0	0.472229	0.786845
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S	26.0	0.321438	-0.488854

3. 데이터 확인¶

In [ ]:

sns.histplot(titanic['Age_mean_mnmx'], bins = 100)

Out[ ]:

<Axes: xlabel='Age_mean_mnmx', ylabel='Count'>

In [ ]:

sns.histplot(titanic['Fare_stnd'], bins = 100)

Out[ ]:

<Axes: xlabel='Fare_stnd', ylabel='Count'>