[Machine Learning] 타이타닉 생존자 머신러닝 -1-

In [4]:

df_train=pd.read_csv('Dataset/Titanic/train.csv')
df_test=pd.read_csv('Dataset/Titanic/test.csv')

In [5]:

df_train.head()

Out[5]:

	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 [6]:

df_train.describe()

Out[6]:

	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

In [7]:

df_test.describe()

Out[7]:

	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

In [15]:

for col in df_test.columns:
    msg = "column: {:>10}\t Percent of NaN value: {:.2f}%".format(col,100*(df_test[col].isnull().sum()/df_test[col].shape[0]))
    print(msg)

column: PassengerId	 Percent of NaN value: 0.00%
column:     Pclass	 Percent of NaN value: 0.00%
column:       Name	 Percent of NaN value: 0.00%
column:        Sex	 Percent of NaN value: 0.00%
column:        Age	 Percent of NaN value: 20.57%
column:      SibSp	 Percent of NaN value: 0.00%
column:      Parch	 Percent of NaN value: 0.00%
column:     Ticket	 Percent of NaN value: 0.00%
column:       Fare	 Percent of NaN value: 0.24%
column:      Cabin	 Percent of NaN value: 78.23%
column:   Embarked	 Percent of NaN value: 0.00%

In [16]:

 for col in df_train.columns:
    msg = "column: {:>10}\t Percent of NaN value: {:.2f}%".format(col,100*(df_train[col].isnull().sum()/df_train[col].shape[0]))
    print(msg)

column: PassengerId	 Percent of NaN value: 0.00%
column:   Survived	 Percent of NaN value: 0.00%
column:     Pclass	 Percent of NaN value: 0.00%
column:       Name	 Percent of NaN value: 0.00%
column:        Sex	 Percent of NaN value: 0.00%
column:        Age	 Percent of NaN value: 19.87%
column:      SibSp	 Percent of NaN value: 0.00%
column:      Parch	 Percent of NaN value: 0.00%
column:     Ticket	 Percent of NaN value: 0.00%
column:       Fare	 Percent of NaN value: 0.00%
column:      Cabin	 Percent of NaN value: 77.10%
column:   Embarked	 Percent of NaN value: 0.22%

In [17]:

msno.matrix(df=df_train.iloc[:,:], figsize=(8,8), color=(0.8,0.5,0.2))

Out[17]:

<AxesSubplot:>

In [18]:

msno.bar(df=df_train.iloc[:,:],figsize=(8,8),color=(0.8,0.5,0.2))

Out[18]:

<AxesSubplot:>

In [19]:

f,ax=plt.subplots(1,2,figsize=(18,8))

df_train['Survived'].value_counts().plot.pie(explode=[0,0.1], autopct='%1.1f%%',ax=ax[0],shadow=True)
ax[0].set_title('Pie plot - Survived')
ax[0].set_ylabel('')
sns.countplot("Survived",data=df_train,ax=ax[1])
ax[1].set_title("Count plot - Survived")

plt.show()

In [1]:

from IPython.core.display import display, HTML
display(HTML("<style>.container {width:90% !important;}</style>"))

C:\Users\easyd\AppData\Local\Temp\ipykernel_28056\3510566465.py:1: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display
  from IPython.core.display import display, HTML