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titanic_test.csv
0.03MB
titanic_train.csv
0.07MB
# 분류
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df_train = pd.read_csv('titanic_train.csv')
df_test = pd.read_csv('titanic_test.csv')
df_train.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 916 entries, 0 to 915
Data columns (total 13 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 pclass 916 non-null int64
1 survived 916 non-null int64
2 name 916 non-null object
3 sex 916 non-null object
4 age 741 non-null float64
5 sibsp 916 non-null int64
6 parch 916 non-null int64
7 ticket 916 non-null object
8 fare 916 non-null float64
9 cabin 214 non-null object
10 embarked 914 non-null object
11 body 85 non-null float64
12 home.dest 527 non-null object
dtypes: float64(3), int64(4), object(6)
memory usage: 93.2+ KB
df_train['survived'].value_counts()
0 563
1 353
Name: survived, dtype: int64
df_train['survived'].value_counts().plot.bar()
df_train[['pclass','survived']].value_counts().sort_index().plot.bar()
ax = sns.countplot(x='pclass', hue = 'survived', data = df_train)
df_train[['sex','survived']].value_counts().sort_index().plot.bar()
ax = sns.countplot(x='sex', hue = 'survived', data = df_train)
# 분류
# 결측치 처리
- 삭제 : 처리가 쉽지만, 중요정보 삭제
# 변경
- 평균값 또는 중앙값, 최빈값으로 처리
df_train.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 916 entries, 0 to 915
Data columns (total 13 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 pclass 916 non-null int64
1 survived 916 non-null int64
2 name 916 non-null object
3 sex 916 non-null object
4 age 741 non-null float64
5 sibsp 916 non-null int64
6 parch 916 non-null int64
7 ticket 916 non-null object
8 fare 916 non-null float64
9 cabin 214 non-null object
10 embarked 914 non-null object
11 body 85 non-null float64
12 home.dest 527 non-null object
dtypes: float64(3), int64(4), object(6)
memory usage: 93.2+ KB
age_mean = df_train['age'].mean()
age_mean
30.23144399460189
df_train['age'] = df_train['age'].fillna(age_mean)
df_test['age'] = df_test['age'].fillna(age_mean)
# age_mean = df_train['age'].mean(skipna = False)
df_train['age']
0 13.000000
1 4.000000
2 30.000000
3 30.231444
4 22.000000
...
911 0.170000
912 30.231444
913 30.231444
914 20.000000
915 32.000000
Name: age, Length: 916, dtype: float64
df_train['embarked'].isnull().value_counts()
False 914
True 2
Name: embarked, dtype: int64
replace_embarked = df_train['embarked'].value_counts().index[0]
df_train['embarked'] = df_train['embarked'].fillna(replace_embarked)
df_test['embarked'] = df_test['embarked'].fillna(replace_embarked)
df_train['embarked']
0 S
1 S
2 C
3 Q
4 Q
..
911 S
912 S
913 Q
914 S
915 Q
Name: embarked, Length: 916, dtype: object
df_train = df_train.drop(['name','ticket','body','cabin','home.dest'], axis=1)
df_test = df_test.drop(['name','ticket','body','cabin','home.dest'], axis=1)
df_test.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 393 entries, 0 to 392
Data columns (total 8 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 pclass 393 non-null int64
1 survived 393 non-null int64
2 sex 393 non-null object
3 age 393 non-null float64
4 sibsp 393 non-null int64
5 parch 393 non-null int64
6 fare 393 non-null float64
7 embarked 393 non-null object
dtypes: float64(2), int64(4), object(2)
memory usage: 24.7+ KB
whole_df = df_train.append(df_test)
whole_df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1309 entries, 0 to 392
Data columns (total 8 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 pclass 1309 non-null int64
1 survived 1309 non-null int64
2 sex 1309 non-null object
3 age 1309 non-null float64
4 sibsp 1309 non-null int64
5 parch 1309 non-null int64
6 fare 1309 non-null float64
7 embarked 1309 non-null object
dtypes: float64(2), int64(4), object(2)
memory usage: 92.0+ KB
train_num = len(df_train)
whole_df_encoded = pd.get_dummies(whole_df)
whole_df_encoded.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1309 entries, 0 to 392
Data columns (total 11 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 pclass 1309 non-null int64
1 survived 1309 non-null int64
2 age 1309 non-null float64
3 sibsp 1309 non-null int64
4 parch 1309 non-null int64
5 fare 1309 non-null float64
6 sex_female 1309 non-null uint8
7 sex_male 1309 non-null uint8
8 embarked_C 1309 non-null uint8
9 embarked_Q 1309 non-null uint8
10 embarked_S 1309 non-null uint8
dtypes: float64(2), int64(4), uint8(5)
memory usage: 78.0 KB
df_train = whole_df_encoded[:train_num]
df_test = whole_df_encoded[train_num:]
df_train.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 916 entries, 0 to 915
Data columns (total 11 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 pclass 916 non-null int64
1 survived 916 non-null int64
2 age 916 non-null float64
3 sibsp 916 non-null int64
4 parch 916 non-null int64
5 fare 916 non-null float64
6 sex_female 916 non-null uint8
7 sex_male 916 non-null uint8
8 embarked_C 916 non-null uint8
9 embarked_Q 916 non-null uint8
10 embarked_S 916 non-null uint8
dtypes: float64(2), int64(4), uint8(5)
memory usage: 54.6 KB
df_test.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 393 entries, 0 to 392
Data columns (total 11 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 pclass 393 non-null int64
1 survived 393 non-null int64
2 age 393 non-null float64
3 sibsp 393 non-null int64
4 parch 393 non-null int64
5 fare 393 non-null float64
6 sex_female 393 non-null uint8
7 sex_male 393 non-null uint8
8 embarked_C 393 non-null uint8
9 embarked_Q 393 non-null uint8
10 embarked_S 393 non-null uint8
dtypes: float64(2), int64(4), uint8(5)
memory usage: 23.4 KB
y_train = df_train['survived'].values
x_train = df_train.loc[:,df_train.columns != 'survived'].values
y_test = df_test['survived'].values
x_test = df_train.loc[:,df_train.columns != 'survived'].values
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression(random_state=0)
lr.fit(x_train, y_train)
y_pred = lr.predict(x_test)
# 평가
from sklearn.metrics import confusion_matrix
confmat = confusion_matrix(y_true = y_test, y_pred = y_pred)
print(confmat)
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
print('정확도(accuracy) : %.2f'% accuracy_score(y_test, y_pred))
print('정밀도(precision) : %.3f'% precision_score(y_test, y_pred))
print('재현율(recall) : %.3f'% recall_score(y_test, y_pred))
print('F1-score : %.3f'% f1_score(y_test, y_pred))
# f= 2*(정밀도*재현율)/(정밀도+재현율)
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