타이타닉 생존자예측 데이터분석
Kaggle의 타이타닉 데이터를 받아 주피터노트북에서
파이썬을 이용하여 생존자를 예측하였습니다.
필요한 데이터만 추출하여 전처리를 하였고, 사이킷런의 의사결정트리를 이용하여 모델을 만들었습니다.
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
test=pd.read_csv("C:/Users/User/Downloads/titanic/test.csv")
train=pd.read_csv("C:/Users/User/Downloads/titanic/train.csv")
test.head()
| PassengerId | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 892 | 3 | Kelly, Mr. James | male | 34.5 | 0 | 0 | 330911 | 7.8292 | NaN | Q |
| 1 | 893 | 3 | Wilkes, Mrs. James (Ellen Needs) | female | 47.0 | 1 | 0 | 363272 | 7.0000 | NaN | S |
| 2 | 894 | 2 | Myles, Mr. Thomas Francis | male | 62.0 | 0 | 0 | 240276 | 9.6875 | NaN | Q |
| 3 | 895 | 3 | Wirz, Mr. Albert | male | 27.0 | 0 | 0 | 315154 | 8.6625 | NaN | S |
| 4 | 896 | 3 | Hirvonen, Mrs. Alexander (Helga E Lindqvist) | female | 22.0 | 1 | 1 | 3101298 | 12.2875 | NaN | S |
train.head()
| 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 |
train.shape
(891, 21)
test.shape
(418, 11)
test.describe()
| 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 |
train.describe()
| PassengerId | Survived | Pclass | Age | SibSp | Parch | Fare | Sex_encode | Fare_fillin | fares | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 891.000000 | 891.000000 | 891.000000 | 714.000000 | 891.000000 | 891.000000 | 891.000000 | 891.000000 | 891.000000 | 891.000000 |
| mean | 446.000000 | 0.383838 | 2.308642 | 29.699118 | 0.523008 | 0.381594 | 32.204208 | 0.647587 | 32.843148 | 0.702581 |
| std | 257.353842 | 0.486592 | 0.836071 | 14.526497 | 1.102743 | 0.806057 | 49.693429 | 0.477990 | 49.657185 | 0.784817 |
| min | 1.000000 | 0.000000 | 1.000000 | 0.420000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 4.012500 | 0.000000 |
| 25% | 223.500000 | 0.000000 | 2.000000 | 20.125000 | 0.000000 | 0.000000 | 7.910400 | 0.000000 | 7.925000 | 0.000000 |
| 50% | 446.000000 | 0.000000 | 3.000000 | 28.000000 | 0.000000 | 0.000000 | 14.454200 | 1.000000 | 14.500000 | 0.000000 |
| 75% | 668.500000 | 1.000000 | 3.000000 | 38.000000 | 1.000000 | 0.000000 | 31.000000 | 1.000000 | 31.275000 | 1.000000 |
| max | 891.000000 | 1.000000 | 3.000000 | 80.000000 | 8.000000 | 6.000000 | 512.329200 | 1.000000 | 512.329200 | 2.000000 |
data preprocessing
test.isnull().sum()
PassengerId 0
Pclass 0
Name 0
Sex 0
Age 86
SibSp 0
Parch 0
Ticket 0
Fare 1
Cabin 327
Embarked 0
title 0
young&women 0
man 0
Embarked_C 0
Embarked_S 0
Embarked_Q 0
Sex_encode 0
Fare_fillin 1
FamilySize 0
FamilyType 0
Single 0
Nuclear 0
Big 0
fares 63
dtype: int64
def get_title(name):
return name.split(', ')[1].split('.')[0]
train["title"]=train["Name"].apply(get_title)
test["title"]=test["Name"].apply(get_title)
print(train["title"].unique(),test["title"].unique())
['Mr' 'Mrs' 'Miss' 'Master' 'Don' 'Rev' 'Dr' 'Mme' 'Ms' 'Major' 'Lady'
'Sir' 'Mlle' 'Col' 'Capt' 'the Countess' 'Jonkheer'] ['Mr' 'Mrs' 'Miss' 'Master' 'Ms' 'Col' 'Rev' 'Dr' 'Dona']
f, ax = plt.subplots(2, 2, figsize=(20,15))
sns.countplot('Embarked', data=train,ax=ax[0,0])
ax[0,0].set_title('No. Of Passengers Boarded')
sns.countplot('Embarked',hue='Sex',data=train,ax=ax[0,1])
ax[0,1].set_title('Male-Female Split for Embarked')
sns.countplot('Embarked',hue='Survived',data=train,ax=ax[1,0])
ax[1,0].set_title('Embarked vs Survived')
sns.countplot('Embarked',hue='Pclass',data=train,ax=ax[1,1])
ax[1,1].set_title('Embarked vs Pclass')
plt.show()
sns.countplot(x="title", hue="Survived",data=train)
<matplotlib.axes._subplots.AxesSubplot at 0x27093396088>
- train으로 survived와 변수와의 상관관계를 밝히고 test로 넘어간다
train["young&women"]=(train["title"]=="Mrs") | (train["title"]=="Miss") | (train["title"]=="Master") | (train["title"]=="Ms")\
|(train["title"]=="Lady") | (train["title"]=="the Countess") |(train["title"]=="Mme") | (train["title"]=="Mlle") |(train["title"]=="Dr")
train["man"]=(train["title"]=="Mr")|(train["title"]=="Col")|(train["title"]=="Rev")|(train["title"]=="Don")
test["young&women"]=(test["title"]=="Mrs") | (test["title"]=="Miss") | (test["title"]=="Master")| (test["title"]=="Ms")\
|(test["title"]=="Dona")|(train["title"]=="Dr")
test["man"]= (test["title"]=="Mr")|(test["title"]=="Col")|(test["title"]=="Rev")
print(train.loc[886])
PassengerId 887
Survived 0
Pclass 2
Name Montvila, Rev. Juozas
Sex male
Age 27
SibSp 0
Parch 0
Ticket 211536
Fare 13
Cabin NaN
Embarked S
title Rev
young&women False
man True
Name: 886, dtype: object
train.isnull().sum()
PassengerId 0
Survived 0
Pclass 0
Name 0
Sex 0
Age 177
SibSp 0
Parch 0
Ticket 0
Fare 0
Cabin 687
Embarked 2
title 0
young&women 0
man 0
dtype: int64
sns.countplot(data=train, x="Sex", hue="Survived")
<matplotlib.axes._subplots.AxesSubplot at 0x270951e5d08>
pd.pivot_table(train, index="title", values="Survived")
| Survived | |
|---|---|
| title | |
| Capt | 0.000000 |
| Col | 0.500000 |
| Don | 0.000000 |
| Dr | 0.428571 |
| Jonkheer | 0.000000 |
| Lady | 1.000000 |
| Major | 0.500000 |
| Master | 0.575000 |
| Miss | 0.697802 |
| Mlle | 1.000000 |
| Mme | 1.000000 |
| Mr | 0.156673 |
| Mrs | 0.792000 |
| Ms | 1.000000 |
| Rev | 0.000000 |
| Sir | 1.000000 |
| the Countess | 1.000000 |
- 여성승객이 남성승객보다 생존율이높다
- 성별은 생존률에 유의미한 변수이다
train.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 15 columns):
PassengerId 891 non-null int64
Survived 891 non-null int64
Pclass 891 non-null int64
Name 891 non-null object
Sex 891 non-null object
Age 714 non-null float64
SibSp 891 non-null int64
Parch 891 non-null int64
Ticket 891 non-null object
Fare 891 non-null float64
Cabin 204 non-null object
Embarked 889 non-null object
title 891 non-null object
young&women 891 non-null bool
man 891 non-null bool
dtypes: bool(2), float64(2), int64(5), object(6)
memory usage: 92.4+ KB
EDA 탐색적 자료 분석
sns.countplot(data=train, x="Pclass", hue="Survived")
<matplotlib.axes._subplots.AxesSubplot at 0x270963cd1c8>
pd.pivot_table(train, index="Pclass", values="Survived")
| Survived | |
|---|---|
| Pclass | |
| 1 | 0.629630 |
| 2 | 0.472826 |
| 3 | 0.242363 |
- 3등객실에 비해 1등객실의 생존률이 높다
- 객실 등급은 생존률에 유의미한 변수이다
sns.countplot(data=train, x="Embarked", hue="Survived")
<matplotlib.axes._subplots.AxesSubplot at 0x27096426cc8>
pd.pivot_table(train, index="Embarked", values="Survived")
| Survived | |
|---|---|
| Embarked | |
| C | 0.553571 |
| Q | 0.389610 |
| S | 0.336957 |
train["Embarked"]=train["Embarked"].fillna("S")
train["Embarked_C"] = train["Embarked"] == "C"
train["Embarked_S"] = train["Embarked"] == "S"
train["Embarked_Q"] = train["Embarked"] == "Q"
train[["Embarked", "Embarked_C", "Embarked_S", "Embarked_Q"]].head()
| Embarked | Embarked_C | Embarked_S | Embarked_Q | |
|---|---|---|---|---|
| 0 | S | False | True | False |
| 1 | C | True | False | False |
| 2 | S | False | True | False |
| 3 | S | False | True | False |
| 4 | S | False | True | False |
train.shape
(891, 21)
train.head()
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | title | young&women | man | Embarked_C | Embarked_S | Embarked_Q | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22.0 | 1 | 0 | A/5 21171 | 7.2500 | NaN | S | Mr | False | True | False | True | False |
| 1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38.0 | 1 | 0 | PC 17599 | 71.2833 | C85 | C | Mrs | True | False | True | False | False |
| 2 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26.0 | 0 | 0 | STON/O2. 3101282 | 7.9250 | NaN | S | Miss | True | False | False | True | False |
| 3 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35.0 | 1 | 0 | 113803 | 53.1000 | C123 | S | Mrs | True | False | False | True | False |
| 4 | 5 | 0 | 3 | Allen, Mr. William Henry | male | 35.0 | 0 | 0 | 373450 | 8.0500 | NaN | S | Mr | False | True | False | True | False |
test["Embarked_C"] = test["Embarked"] == "C"
test["Embarked_S"] = test["Embarked"] == "S"
test["Embarked_Q"] = test["Embarked"] == "Q"
test[["Embarked", "Embarked_C", "Embarked_S", "Embarked_Q"]].head()
| Embarked | Embarked_C | Embarked_S | Embarked_Q | |
|---|---|---|---|---|
| 0 | Q | False | False | True |
| 1 | S | False | True | False |
| 2 | Q | False | False | True |
| 3 | S | False | True | False |
| 4 | S | False | True | False |
sns.lmplot(data=train, x="Age", y="Fare",hue="Survived", fit_reg=False)
<seaborn.axisgrid.FacetGrid at 0x2709668d8c8>
low_fare = train[train["Fare"] < 500]
high_fare=train[train["Fare"]>=500]
high_fare
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | title | young&women | man | Embarked_C | Embarked_S | Embarked_Q | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 258 | 259 | 1 | 1 | Ward, Miss. Anna | female | 35.0 | 0 | 0 | PC 17755 | 512.3292 | NaN | C | Miss | True | False | True | False | False |
| 679 | 680 | 1 | 1 | Cardeza, Mr. Thomas Drake Martinez | male | 36.0 | 0 | 1 | PC 17755 | 512.3292 | B51 B53 B55 | C | Mr | False | True | True | False | False |
| 737 | 738 | 1 | 1 | Lesurer, Mr. Gustave J | male | 35.0 | 0 | 0 | PC 17755 | 512.3292 | B101 | C | Mr | False | True | True | False | False |
sns.lmplot(data=low_fare, x="Age", y="Fare", hue="Survived", fit_reg=False)
<seaborn.axisgrid.FacetGrid at 0x2709653d748>
test.loc[test["Sex"] == "male", "Sex_encode"] = 1
test.loc[test["Sex"] == "female", "Sex_encode"] = 0
train.loc[train["Sex"] == "male", "Sex_encode"] =1
train.loc[train["Sex"] == "female", "Sex_encode"] = 0
train[["Sex", "Sex_encode"]].head()
| Sex | Sex_encode | |
|---|---|---|
| 0 | male | 1.0 |
| 1 | female | 0.0 |
| 2 | female | 0.0 |
| 3 | female | 0.0 |
| 4 | male | 1.0 |
test[["Sex", "Sex_encode"]].head()
| Sex | Sex_encode | |
|---|---|---|
| 0 | male | 1.0 |
| 1 | female | 0.0 |
| 2 | male | 1.0 |
| 3 | male | 1.0 |
| 4 | female | 0.0 |
train["Fare_fillin"] = train["Fare"]
test["Fare_fillin"] = test["Fare"]
third_fare_train=train.loc[train["Pclass"]==3, "Fare"]
third_fare_test=test.loc[test["Pclass"]==3, "Fare"]
print(third_fare_train.median(),third_fare_test.median())
8.05 7.8958
second_fare_train=train.loc[train["Pclass"]==2, "Fare"]
second_fare_test=test.loc[test["Pclass"]==2, "Fare"]
print(second_fare_train.median(),second_fare_test.median())
14.25 15.75
first_fare_train=train.loc[(train["Pclass"]==1) & (train["Fare"]<300), "Fare"]
first_fare_test=test.loc[(test["Pclass"]==1)&(test["Fare"]<300), "Fare"]
print(first_fare_train.median(),first_fare_test.median())
57.9792 60.0
test.loc[((test["Fare"]==0.0 )| (test["Fare"].isnull()))& (test["Pclass"]==1), "Fare_fillin"] = first_fare_test.median()
train.loc[(train["Fare"]==0.0 )| (train["Pclass"]==1), "Fare_fillin"] = first_fare_train.median()
test.loc[((test["Fare"]==0.0) |(test["Fare"].isnull()))& (test["Pclass"]==2), "Fare_fillin"] = second_fare_test.median()
train.loc[(train["Fare"]==0.0)| (train["Pclass"]==2), "Fare_fillin"] = second_fare_train.median()
test.loc[((test["Fare"]==0.0) |(test["Fare"].isnull()))&( test["Pclass"]==3), "Fare_fillin"] = third_fare_test.median()
train.loc[(train["Fare"]==0.0) | (train["Pclass"]==3), "Fare_fillin"] = third_fare_train.median()
train.isnull().sum()
PassengerId 0
Survived 0
Pclass 0
Name 0
Sex 0
Age 177
SibSp 0
Parch 0
Ticket 0
Fare 0
Cabin 687
Embarked 0
title 0
young&women 0
man 0
Embarked_C 0
Embarked_S 0
Embarked_Q 0
Sex_encode 0
Fare_fillin 0
fares 0
FamilySize 0
FamilyType 0
Single 0
Nuclear 0
Big 0
dtype: int64
sns.kdeplot(train["Survived"], train["Sex_encode"])
<matplotlib.axes._subplots.AxesSubplot at 0x27095236708>
train.loc[train["Fare_fillin"]<10, "fares"]=0
train.loc[(train["Fare_fillin"]>=10) & (train["Fare_fillin"]<=35), "fares"]=1
train.loc[(train["Fare_fillin"]>35), "fares"]=2
test.loc[test["Fare_fillin"]<10, "fares"]=0
test.loc[(test["Fare_fillin"]>=10) & (test["Fare_fillin"]<=35), "fares"]=1
test.loc[(test["Fare_fillin"]>35), "fares"]=2
pd.pivot_table(train, index="fares", values="Survived")
| Survived | |
|---|---|
| fares | |
| 0.0 | 0.237052 |
| 1.0 | 0.488764 |
| 2.0 | 0.644550 |
train.corr()#상관관계 매트릭스
| PassengerId | Survived | Pclass | Age | SibSp | Parch | Fare | young&women | man | Embarked_C | Embarked_S | Embarked_Q | Sex_encode | Fare_fillin | fares | FamilySize | Single | Nuclear | Big | mysex | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PassengerId | 1.000000 | -0.005007 | -0.035144 | 0.036847 | -0.057527 | -0.001652 | 0.012658 | -0.049744 | 0.041419 | -0.001205 | 0.022204 | -0.033606 | 0.042939 | 0.023265 | 0.021634 | -0.040143 | 0.057462 | -0.028976 | -0.057055 | -0.041419 |
| Survived | -0.005007 | 1.000000 | -0.338481 | -0.077221 | -0.035322 | 0.081629 | 0.257307 | 0.563511 | -0.562671 | 0.168240 | -0.149683 | 0.003650 | -0.543351 | 0.319530 | 0.356638 | 0.016639 | -0.203367 | 0.279855 | -0.125147 | 0.562671 |
| Pclass | -0.035144 | -0.338481 | 1.000000 | -0.369226 | 0.083081 | 0.018443 | -0.549500 | -0.109791 | 0.127425 | -0.243292 | 0.074053 | 0.221009 | 0.131900 | -0.917824 | -0.979271 | 0.065997 | 0.135207 | -0.223551 | 0.152366 | -0.127425 |
| Age | 0.036847 | -0.077221 | -0.369226 | 1.000000 | -0.308247 | -0.189119 | 0.096067 | -0.256904 | 0.235802 | 0.036261 | -0.023233 | -0.022405 | 0.093254 | 0.357485 | 0.363850 | -0.301914 | 0.198270 | -0.083059 | -0.226521 | -0.235802 |
| SibSp | -0.057527 | -0.035322 | 0.083081 | -0.308247 | 1.000000 | 0.414838 | 0.159651 | 0.260895 | -0.259057 | -0.059528 | 0.068734 | -0.026354 | -0.114631 | -0.055891 | -0.073128 | 0.890712 | -0.584471 | 0.213225 | 0.730691 | 0.259057 |
| Parch | -0.001652 | 0.081629 | 0.018443 | -0.189119 | 0.414838 | 1.000000 | 0.216225 | 0.345308 | -0.341989 | -0.011069 | 0.060814 | -0.081228 | -0.245489 | -0.010963 | -0.008291 | 0.783111 | -0.583398 | 0.265863 | 0.631523 | 0.341989 |
| Fare | 0.012658 | 0.257307 | -0.549500 | 0.096067 | 0.159651 | 0.216225 | 1.000000 | 0.187869 | -0.188564 | 0.269335 | -0.162184 | -0.117216 | -0.182333 | 0.608117 | 0.565329 | 0.217138 | -0.271832 | 0.205527 | 0.143636 | 0.188564 |
| young&women | -0.049744 | 0.563511 | -0.109791 | -0.256904 | 0.260895 | 0.345308 | 0.187869 | 1.000000 | -0.988440 | 0.070876 | -0.112746 | 0.080495 | -0.895928 | 0.097330 | 0.127915 | 0.350823 | -0.406498 | 0.297336 | 0.233257 | 0.988440 |
| man | 0.041419 | -0.562671 | 0.127425 | 0.235802 | -0.259057 | -0.341989 | -0.188564 | -0.988440 | 1.000000 | -0.071057 | 0.110586 | -0.076811 | 0.885571 | -0.111956 | -0.140334 | -0.347908 | 0.405681 | -0.298450 | -0.229632 | -1.000000 |
| Embarked_C | -0.001205 | 0.168240 | -0.243292 | 0.036261 | -0.059528 | -0.011069 | 0.269335 | 0.070876 | -0.071057 | 1.000000 | -0.782742 | -0.148258 | -0.082853 | 0.299075 | 0.254504 | -0.046215 | -0.095298 | 0.158586 | -0.109274 | 0.071057 |
| Embarked_S | 0.022204 | -0.149683 | 0.074053 | -0.023233 | 0.068734 | 0.060814 | -0.162184 | -0.112746 | 0.110586 | -0.782742 | 1.000000 | -0.499421 | 0.119224 | -0.154113 | -0.087581 | 0.077359 | 0.029074 | -0.084120 | 0.099265 | -0.110586 |
| Embarked_Q | -0.033606 | 0.003650 | 0.221009 | -0.022405 | -0.026354 | -0.081228 | -0.117216 | 0.080495 | -0.076811 | -0.148258 | -0.499421 | 1.000000 | -0.074115 | -0.171442 | -0.215120 | -0.058592 | 0.086464 | -0.087093 | -0.005620 | 0.076811 |
| Sex_encode | 0.042939 | -0.543351 | 0.131900 | 0.093254 | -0.114631 | -0.245489 | -0.182333 | -0.895928 | 0.885571 | -0.082853 | 0.119224 | -0.074115 | 1.000000 | -0.120790 | -0.148251 | -0.200988 | 0.303646 | -0.260747 | -0.102954 | -0.885571 |
| Fare_fillin | 0.023265 | 0.319530 | -0.917824 | 0.357485 | -0.055891 | -0.010963 | 0.608117 | 0.097330 | -0.111956 | 0.299075 | -0.154113 | -0.171442 | -0.120790 | 1.000000 | 0.934535 | -0.043677 | -0.134716 | 0.198030 | -0.106230 | 0.111956 |
| fares | 0.021634 | 0.356638 | -0.979271 | 0.363850 | -0.073128 | -0.008291 | 0.565329 | 0.127915 | -0.140334 | 0.254504 | -0.087581 | -0.215120 | -0.148251 | 0.934535 | 1.000000 | -0.054123 | -0.153169 | 0.239358 | -0.146981 | 0.140334 |
| FamilySize | -0.040143 | 0.016639 | 0.065997 | -0.301914 | 0.890712 | 0.783111 | 0.217138 | 0.350823 | -0.347908 | -0.046215 | 0.077359 | -0.058592 | -0.200988 | -0.043677 | -0.054123 | 1.000000 | -0.690922 | 0.278553 | 0.814901 | 0.347908 |
| Single | 0.057462 | -0.203367 | 0.135207 | 0.198270 | -0.584471 | -0.583398 | -0.271832 | -0.406498 | 0.405681 | -0.095298 | 0.029074 | 0.086464 | 0.303646 | -0.134716 | -0.153169 | -0.690922 | 1.000000 | -0.859931 | -0.336825 | -0.405681 |
| Nuclear | -0.028976 | 0.279855 | -0.223551 | -0.083059 | 0.213225 | 0.265863 | 0.205527 | 0.297336 | -0.298450 | 0.158586 | -0.084120 | -0.087093 | -0.260747 | 0.198030 | 0.239358 | 0.278553 | -0.859931 | 1.000000 | -0.190940 | 0.298450 |
| Big | -0.057055 | -0.125147 | 0.152366 | -0.226521 | 0.730691 | 0.631523 | 0.143636 | 0.233257 | -0.229632 | -0.109274 | 0.099265 | -0.005620 | -0.102954 | -0.106230 | -0.146981 | 0.814901 | -0.336825 | -0.190940 | 1.000000 | 0.229632 |
| mysex | -0.041419 | 0.562671 | -0.127425 | -0.235802 | 0.259057 | 0.341989 | 0.188564 | 0.988440 | -1.000000 | 0.071057 | -0.110586 | 0.076811 | -0.885571 | 0.111956 | 0.140334 | 0.347908 | -0.405681 | 0.298450 | 0.229632 | 1.000000 |
train["FamilySize"] = train["SibSp"] + train["Parch"] + 1
print(train.shape)
(891, 26)
test["FamilySize"] = test["SibSp"] + test["Parch"] + 1
print(test.shape)
(418, 20)
sns.countplot(data=train, x="FamilySize", hue="Survived")
<matplotlib.axes._subplots.AxesSubplot at 0x270967078c8>
train.loc[train["FamilySize"] == 1, "FamilyType"] = "Single"
train.loc[(train["FamilySize"] > 1) & (train["FamilySize"] < 5), "FamilyType"] = "Nuclear"
train.loc[train["FamilySize"] >= 5, "FamilyType"] = "Big"
print(train.shape)
train[["FamilySize", "FamilyType"]].head(10)
(891, 23)
| FamilySize | FamilyType | |
|---|---|---|
| 0 | 2 | Nuclear |
| 1 | 2 | Nuclear |
| 2 | 1 | Single |
| 3 | 2 | Nuclear |
| 4 | 1 | Single |
| 5 | 1 | Single |
| 6 | 1 | Single |
| 7 | 5 | Big |
| 8 | 3 | Nuclear |
| 9 | 2 | Nuclear |
test.loc[train["FamilySize"] == 1, "FamilyType"] = "Single"
test.loc[(train["FamilySize"] > 1) & (train["FamilySize"] < 5), "FamilyType"] = "Nuclear"
test.loc[train["FamilySize"] >= 5, "FamilyType"] = "Big"
sns.countplot(data=train, x="FamilyType", hue="Survived")
<matplotlib.axes._subplots.AxesSubplot at 0x270967ac508>
pd.pivot_table(data=train, index="FamilyType", values="Survived")
| Survived | |
|---|---|
| FamilyType | |
| Big | 0.161290 |
| Nuclear | 0.578767 |
| Single | 0.303538 |
train["Single"] = train["FamilySize"] == 1
train["Nuclear"] = (train["FamilySize"] > 1) & (train["FamilySize"] < 5)
train["Big"] = train["FamilySize"] >= 5
print(train.shape)
train[["FamilySize", "Single", "Nuclear", "Big"]].head(10)
(891, 26)
| FamilySize | Single | Nuclear | Big | |
|---|---|---|---|---|
| 0 | 2 | False | True | False |
| 1 | 2 | False | True | False |
| 2 | 1 | True | False | False |
| 3 | 2 | False | True | False |
| 4 | 1 | True | False | False |
| 5 | 1 | True | False | False |
| 6 | 1 | True | False | False |
| 7 | 5 | False | False | True |
| 8 | 3 | False | True | False |
| 9 | 2 | False | True | False |
test["Single"] = test["FamilySize"] == 1
test["Nuclear"] = (test["FamilySize"] > 1) & (test["FamilySize"] < 5)
test["Big"] = test["FamilySize"] >= 5
print(test.shape)
test[["FamilySize", "Single", "Nuclear", "Big"]].head(10)
(418, 24)
| FamilySize | Single | Nuclear | Big | |
|---|---|---|---|---|
| 0 | 1 | True | False | False |
| 1 | 2 | False | True | False |
| 2 | 1 | True | False | False |
| 3 | 1 | True | False | False |
| 4 | 3 | False | True | False |
| 5 | 1 | True | False | False |
| 6 | 1 | True | False | False |
| 7 | 3 | False | True | False |
| 8 | 1 | True | False | False |
| 9 | 3 | False | True | False |
train.corr()
| PassengerId | Survived | Pclass | Age | SibSp | Parch | Fare | young&women | man | Embarked_C | Embarked_S | Embarked_Q | Sex_encode | Fare_fillin | fares | FamilySize | Single | Nuclear | Big | mysex | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PassengerId | 1.000000 | -0.005007 | -0.035144 | 0.036847 | -0.057527 | -0.001652 | 0.012658 | -0.049744 | 0.041419 | -0.001205 | 0.022204 | -0.033606 | 0.042939 | 0.023267 | 0.021634 | -0.040143 | 0.057462 | -0.028976 | -0.057055 | 0.041419 |
| Survived | -0.005007 | 1.000000 | -0.338481 | -0.077221 | -0.035322 | 0.081629 | 0.257307 | 0.563511 | -0.562671 | 0.168240 | -0.149683 | 0.003650 | -0.543351 | 0.317052 | 0.356638 | 0.016639 | -0.203367 | 0.279855 | -0.125147 | -0.562671 |
| Pclass | -0.035144 | -0.338481 | 1.000000 | -0.369226 | 0.083081 | 0.018443 | -0.549500 | -0.109791 | 0.127425 | -0.243292 | 0.074053 | 0.221009 | 0.131900 | -0.912606 | -0.979271 | 0.065997 | 0.135207 | -0.223551 | 0.152366 | 0.127425 |
| Age | 0.036847 | -0.077221 | -0.369226 | 1.000000 | -0.308247 | -0.189119 | 0.096067 | -0.256904 | 0.235802 | 0.036261 | -0.023233 | -0.022405 | 0.093254 | 0.356091 | 0.363850 | -0.301914 | 0.198270 | -0.083059 | -0.226521 | 0.235802 |
| SibSp | -0.057527 | -0.035322 | 0.083081 | -0.308247 | 1.000000 | 0.414838 | 0.159651 | 0.260895 | -0.259057 | -0.059528 | 0.068734 | -0.026354 | -0.114631 | -0.055006 | -0.073128 | 0.890712 | -0.584471 | 0.213225 | 0.730691 | -0.259057 |
| Parch | -0.001652 | 0.081629 | 0.018443 | -0.189119 | 0.414838 | 1.000000 | 0.216225 | 0.345308 | -0.341989 | -0.011069 | 0.060814 | -0.081228 | -0.245489 | -0.011044 | -0.008291 | 0.783111 | -0.583398 | 0.265863 | 0.631523 | -0.341989 |
| Fare | 0.012658 | 0.257307 | -0.549500 | 0.096067 | 0.159651 | 0.216225 | 1.000000 | 0.187869 | -0.188564 | 0.269335 | -0.162184 | -0.117216 | -0.182333 | 0.608175 | 0.565329 | 0.217138 | -0.271832 | 0.205527 | 0.143636 | -0.188564 |
| young&women | -0.049744 | 0.563511 | -0.109791 | -0.256904 | 0.260895 | 0.345308 | 0.187869 | 1.000000 | -0.988440 | 0.070876 | -0.112746 | 0.080495 | -0.895928 | 0.095763 | 0.127915 | 0.350823 | -0.406498 | 0.297336 | 0.233257 | -0.988440 |
| man | 0.041419 | -0.562671 | 0.127425 | 0.235802 | -0.259057 | -0.341989 | -0.188564 | -0.988440 | 1.000000 | -0.071057 | 0.110586 | -0.076811 | 0.885571 | -0.110441 | -0.140334 | -0.347908 | 0.405681 | -0.298450 | -0.229632 | 1.000000 |
| Embarked_C | -0.001205 | 0.168240 | -0.243292 | 0.036261 | -0.059528 | -0.011069 | 0.269335 | 0.070876 | -0.071057 | 1.000000 | -0.782742 | -0.148258 | -0.082853 | 0.300093 | 0.254504 | -0.046215 | -0.095298 | 0.158586 | -0.109274 | -0.071057 |
| Embarked_S | 0.022204 | -0.149683 | 0.074053 | -0.023233 | 0.068734 | 0.060814 | -0.162184 | -0.112746 | 0.110586 | -0.782742 | 1.000000 | -0.499421 | 0.119224 | -0.156471 | -0.087581 | 0.077359 | 0.029074 | -0.084120 | 0.099265 | 0.110586 |
| Embarked_Q | -0.033606 | 0.003650 | 0.221009 | -0.022405 | -0.026354 | -0.081228 | -0.117216 | 0.080495 | -0.076811 | -0.148258 | -0.499421 | 1.000000 | -0.074115 | -0.169112 | -0.215120 | -0.058592 | 0.086464 | -0.087093 | -0.005620 | -0.076811 |
| Sex_encode | 0.042939 | -0.543351 | 0.131900 | 0.093254 | -0.114631 | -0.245489 | -0.182333 | -0.895928 | 0.885571 | -0.082853 | 0.119224 | -0.074115 | 1.000000 | -0.119288 | -0.148251 | -0.200988 | 0.303646 | -0.260747 | -0.102954 | 0.885571 |
| Fare_fillin | 0.023267 | 0.317052 | -0.912606 | 0.356091 | -0.055006 | -0.011044 | 0.608175 | 0.095763 | -0.110441 | 0.300093 | -0.156471 | -0.169112 | -0.119288 | 1.000000 | 0.929100 | -0.043112 | -0.133553 | 0.195723 | -0.104210 | -0.110441 |
| fares | 0.021634 | 0.356638 | -0.979271 | 0.363850 | -0.073128 | -0.008291 | 0.565329 | 0.127915 | -0.140334 | 0.254504 | -0.087581 | -0.215120 | -0.148251 | 0.929100 | 1.000000 | -0.054123 | -0.153169 | 0.239358 | -0.146981 | -0.140334 |
| FamilySize | -0.040143 | 0.016639 | 0.065997 | -0.301914 | 0.890712 | 0.783111 | 0.217138 | 0.350823 | -0.347908 | -0.046215 | 0.077359 | -0.058592 | -0.200988 | -0.043112 | -0.054123 | 1.000000 | -0.690922 | 0.278553 | 0.814901 | -0.347908 |
| Single | 0.057462 | -0.203367 | 0.135207 | 0.198270 | -0.584471 | -0.583398 | -0.271832 | -0.406498 | 0.405681 | -0.095298 | 0.029074 | 0.086464 | 0.303646 | -0.133553 | -0.153169 | -0.690922 | 1.000000 | -0.859931 | -0.336825 | 0.405681 |
| Nuclear | -0.028976 | 0.279855 | -0.223551 | -0.083059 | 0.213225 | 0.265863 | 0.205527 | 0.297336 | -0.298450 | 0.158586 | -0.084120 | -0.087093 | -0.260747 | 0.195723 | 0.239358 | 0.278553 | -0.859931 | 1.000000 | -0.190940 | -0.298450 |
| Big | -0.057055 | -0.125147 | 0.152366 | -0.226521 | 0.730691 | 0.631523 | 0.143636 | 0.233257 | -0.229632 | -0.109274 | 0.099265 | -0.005620 | -0.102954 | -0.104210 | -0.146981 | 0.814901 | -0.336825 | -0.190940 | 1.000000 | -0.229632 |
| mysex | 0.041419 | -0.562671 | 0.127425 | 0.235802 | -0.259057 | -0.341989 | -0.188564 | -0.988440 | 1.000000 | -0.071057 | 0.110586 | -0.076811 | 0.885571 | -0.110441 | -0.140334 | -0.347908 | 0.405681 | -0.298450 | -0.229632 | 1.000000 |
#mysex로 young&women일때 1, man일때 0을 대입했는데 점수가 더 낮게나옴
Fitting the model
from sklearn.model_selection import train_test_split
from sklearn import metrics
features = ["Pclass", "man","young&women", "fares",
"Single","Nuclear"]
features
['Pclass', 'man', 'young&women', 'fares', 'Single', 'Nuclear']
label="Survived"
label
'Survived'
x_train = train[features]
x_train.head()
| Pclass | man | young&women | fares | Single | Nuclear | |
|---|---|---|---|---|---|---|
| 0 | 3 | True | False | 0.0 | False | True |
| 1 | 1 | False | True | 2.0 | False | True |
| 2 | 3 | False | True | 0.0 | True | False |
| 3 | 1 | False | True | 2.0 | False | True |
| 4 | 3 | True | False | 0.0 | True | False |
x_train.shape
(891, 6)
x_test=test[features]
x_test.head()
| Pclass | man | young&women | fares | Single | Nuclear | |
|---|---|---|---|---|---|---|
| 0 | 3 | True | False | 0.0 | True | False |
| 1 | 3 | False | True | 0.0 | False | True |
| 2 | 2 | True | False | 0.0 | True | False |
| 3 | 3 | True | False | 0.0 | True | False |
| 4 | 3 | False | True | 1.0 | False | True |
x_test.isnull().sum()
Pclass 0
man 0
young&women 0
fares 0
Single 0
Nuclear 0
dtype: int64
x_test.shape
(418, 6)
y_train=train[label]
y_train.head()
0 0
1 1
2 1
3 1
4 0
Name: Survived, dtype: int64
from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier(max_depth=5)
model
DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=5,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, presort=False,
random_state=None, splitter='best')
model.fit(x_train, y_train)
DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=5,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, presort=False,
random_state=None, splitter='best')
predictions = model.predict(x_test)
predictions.shape
(418,)
predictions
array([0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1,
1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,
1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1,
1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1,
1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,
1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,
1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1,
0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,
0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1,
0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,
1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0,
0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,
1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1,
0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1],
dtype=int64)
submission = pd.read_csv("C:/Users/User/Downloads/titanic/gender_submission.csv", index_col="PassengerId")
submission.head()
| Survived | |
|---|---|
| PassengerId | |
| 892 | 0 |
| 893 | 1 |
| 894 | 0 |
| 895 | 0 |
| 896 | 1 |
submission.shape
(418, 1)
submission["Survived"] = predictions
y_train=predictions
print(metrics.accuracy_score(predictions, y_train))
1.0
submission.head()
| Survived | |
|---|---|
| PassengerId | |
| 892 | 0 |
| 893 | 1 |
| 894 | 0 |
| 895 | 0 |
| 896 | 1 |
submission.to_csv("C:/Users/User/Downloads/titanic/finish.csv")
import pandas_profiling
submission.profile_report()
Tab(children=(HTML(value='<div id="overview-content" class="row variable spacing">\n <div class="row">\n …
Report generated with pandas-profiling.
train.profile_report()
Tab(children=(HTML(value='<div id="overview-content" class="row variable spacing">\n <div class="row">\n …
Report generated with pandas-profiling.
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