실전 데이터 알고리즘 적용¶

  • 데이터 셋 : https://www.kaggle.com/competitions/titanic/
  • 적용 알고리즘 : PCA

01 라이브러리 및 데이터 불러오기¶

In [1]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
In [2]:
tr = pd.read_csv("./titanic/train.csv")
test = pd.read_csv("./titanic/test.csv")

tr.shape, test.shape
Out[2]:
((891, 12), (418, 11))

02 train 데이터 셋을 활용한 PCA 알고리즘 적용 및 활용¶

In [3]:
tr.columns
Out[3]:
Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',
       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],
      dtype='object')

기본 전처리¶

In [4]:
tr.head()
Out[4]:
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
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S
In [5]:
tr.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

결측치 처리¶

In [6]:
tr['Embarked'].value_counts()
Out[6]:
S    644
C    168
Q     77
Name: Embarked, dtype: int64
In [7]:
tr.loc[ tr['Age'].isnull(), 'Age'] =  tr['Age'].mean()
tr.loc[ tr['Embarked'].isnull(), 'Embarked'] ='S'

tr.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          891 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     891 non-null    object 
dtypes: float64(2), int64(5), object(5)
memory usage: 83.7+ KB
In [8]:
di_sex = {"male":0, "female":1}
di_Embarked = {"C":0, "S":1, "Q":2}

tr['Sex_lbl'] = tr['Sex'].map(di_sex)
tr['Embarked_lbl'] = tr['Embarked'].map(di_Embarked)

tr.head()
Out[8]:
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked Sex_lbl Embarked_lbl
0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S 0 1
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C 1 0
2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S 1 1
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S 1 1
4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S 0 1

기본적인 전처리가 필요없는 피처를 선택¶

In [9]:
X = tr.drop(['Survived', 'Name', 'Ticket', 'Cabin', 'Sex', 'Embarked'], axis=1)
y = tr['Survived']

print( X.shape, y.shape )

(891, 8) (891,)
In [10]:
X.head()
Out[10]:
PassengerId Pclass Age SibSp Parch Fare Sex_lbl Embarked_lbl
0 1 3 22.0 1 0 7.2500 0 1
1 2 1 38.0 1 0 71.2833 1 0
2 3 3 26.0 0 0 7.9250 1 1
3 4 1 35.0 1 0 53.1000 1 1
4 5 3 35.0 0 0 8.0500 0 1
  • 현재 변수가 8개, 이를 PCA를 이용하여 3개의 피처를 갖는 주성분으로 만들어보자.
In [11]:
### 데이터 나누기
scaler = StandardScaler()
X_train, X_test, y_train, y_test = train_test_split(X, y, 
                                test_size=0.3, random_state=42)

X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
In [14]:
from sklearn.decomposition import PCA

pca = PCA(n_components=2)
scaler = StandardScaler()

X_train = pca.fit_transform(X_train)
X_test = pca.transform(X_test)

X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
In [15]:
plt.figure(figsize=(8,6))
plt.scatter(X_train[:,0],X_train[:,1],c=y_train,cmap='plasma')
plt.xlabel('First principal component')
plt.ylabel('Second Principal Component')
Out[15]:
Text(0, 0.5, 'Second Principal Component')