Heart Desease Prediction
1. 데이터셋 출처
- 데이터셋 출처 : Kaggle
2. 데이터셋 설명
정말 유명한 데이터셋입니다. 추천 수가 3000이 넘어가고 제출된 분석 코드 수만 1000개가 넘습니다.
3. 데이터셋 분석
시작하기 전에 데이터셋이 준비되어 있어야합니다. 데이터를 실제로 확인하고 싶으시다면 캐글에서 데이터셋을 다운로드 받으시면 됩니다.
1) Variables
| Variable | info |
|---|---|
| age | Age of the patient(환자의 나이) |
| sex | Sex of the patient(환자의 성별) |
| exang | exercise induced angina (1 = yes; 0 = no) |
| (협심증이 운동으로 유발되는지 여부) | |
| ca | number of major vessels (0-3)(주요 혈관의 수) |
| cp | Chest Pain type chest pain type(가슴 통증 유형) |
| Value 1: typical angina(일반적인/전형적인 협심증 유형) | |
| Value 2: atypical angina(예외적인/비전형적인 협심증 유형) | |
| Value 3: non-anginal pain(협심증 아닌 통증 유형) | |
| Value 4: asymptomatic(무증상) | |
| trtbps | resting blood pressure (in mm Hg) |
| (안정된 상태의 혈압)(mmHg => 수은주 밀리미터) | |
| chol | cholestoral in mg/dl fetched via BMI sensor |
| (BMI 센서를 통해 가져온 콜레스테롤 수치) | |
| (mg/dl => 밀리그램/데시리터) | |
| fbs | (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false) |
| (공복 혈당) | |
| rest_ecg | resting electrocardiographic results(안정시 심전도 결과) |
| Value 0: normal(일반적인 경우) | |
| Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV) | |
| (ST-T파 이상이 있는 경우) | |
| Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria | |
| thalach | maximum heart rate achieved(최대 심박수) |
| target | 0= less chance of heart attack |
| 1= more chance of heart attack |
2) Preparation
2.1 Packages
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
2.2 Data
df = pd.read_csv("/Downloads/heart-attack/heart.csv")
2.3 Shape
print("데이터 (행의 수, 차원의 수) : ", df.shape)
데이터 (행의 수, 차원의 수) : (303, 14)
df.head()
| age | sex | cp | trtbps | chol | fbs | restecg | thalachh | exng | oldpeak | slp | caa | thall | output | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 63 | 1 | 3 | 145 | 233 | 1 | 0 | 150 | 0 | 2.3 | 0 | 0 | 1 | 1 |
| 1 | 37 | 1 | 2 | 130 | 250 | 0 | 1 | 187 | 0 | 3.5 | 0 | 0 | 2 | 1 |
| 2 | 41 | 0 | 1 | 130 | 204 | 0 | 0 | 172 | 0 | 1.4 | 2 | 0 | 2 | 1 |
| 3 | 56 | 1 | 1 | 120 | 236 | 0 | 1 | 178 | 0 | 0.8 | 2 | 0 | 2 | 1 |
| 4 | 57 | 0 | 0 | 120 | 354 | 0 | 1 | 163 | 1 | 0.6 | 2 | 0 | 2 | 1 |
추가로 각각의 컬럼별로 고유값의 개수를 체크합니다.
dic = {}
for i in list(df.columns):
dic[i] = df[i].value_counts().shape[0]
pd.DataFrame(dic, index=["unique count"]).transpose()
| unique | count |
|---|---|
| age | 41 |
| sex | 2 |
| cp | 4 |
| trtbps | 49 |
| chol | 152 |
| fbs | 2 |
| restecg | 3 |
| thalachh | 91 |
| exng | 2 |
| oldpeak | 40 |
| slp | 3 |
| caa | 5 |
| thall | 4 |
| output | 2 |
이제 중요한 작업을 해줘야합니다. 바로 범주형 변수와 연속형 변수를 구분하는 것입니다.
# 범주형 변수
cat_cols = ["sex","exng","caa","cp","fbs","restecg","slp","thall"]
# 연속형 변수
con_cols = ["age","trtbps","chol","thalachh","oldpeak"]
target_col = ["output"]
print("범주형: ", cat_cols)
print("연속형: ", con_cols)
print("목표변수(종속변수): ", target_col)
The categorial cols are : ['sex', 'exng', 'caa', 'cp', 'fbs', 'restecg', 'slp', 'thall']
The continuous cols are : ['age', 'trtbps', 'chol', 'thalachh', 'oldpeak']
The target variable is : ['output']
연속형 변수의 기초통계량 정보를 확인합니다.
df[con_cols].describe().transpose()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| age | 303.0 | 54.366337 | 9.082101 | 29.0 | 47.5 | 55.0 | 61.0 | 77.0 |
| trtbps | 303.0 | 131.623762 | 17.538143 | 94.0 | 120.0 | 130.0 | 140.0 | 200.0 |
| chol | 303.0 | 246.264026 | 51.830751 | 126.0 | 211.0 | 240.0 | 274.5 | 564.0 |
| thalachh | 303.0 | 149.646865 | 22.905161 | 71.0 | 133.5 | 153.0 | 166.0 | 202.0 |
| oldpeak | 303.0 | 1.039604 | 1.161075 | 0.0 | 0.0 | 0.8 | 1.6 | 6.2 |
마지막으로 결측값을 확인합니다.
df.isnull().sum()
| variable | count |
|---|---|
| age | 0 |
| sex | 0 |
| cp | 0 |
| trtbps | 0 |
| chol | 0 |
| fbs | 0 |
| restecg | 0 |
| thalachh | 0 |
| exng | 0 |
| oldpeak | 0 |
| slp | 0 |
| caa | 0 |
| thall | 0 |
| output | 0 |
| dtype: int64 | - |
2) EDA(탐색적 데이터 분석)
fig = plt.figure(figsize=(18,15))
gs = fig.add_gridspec(3,3)
gs.update(wspace=0.5, hspace=0.25)
ax0 = fig.add_subplot(gs[0,0])
ax1 = fig.add_subplot(gs[0,1])
ax2 = fig.add_subplot(gs[0,2])
ax3 = fig.add_subplot(gs[1,0])
ax4 = fig.add_subplot(gs[1,1])
ax5 = fig.add_subplot(gs[1,2])
ax6 = fig.add_subplot(gs[2,0])
ax7 = fig.add_subplot(gs[2,1])
ax8 = fig.add_subplot(gs[2,2])
background_color = "#ffffff"
color_palette = ["#D04848","#F3B95F","#FDE767","#6895D2","#da8829"]
fig.patch.set_facecolor(background_color)
ax0.set_facecolor(background_color)
ax1.set_facecolor(background_color)
ax2.set_facecolor(background_color)
ax3.set_facecolor(background_color)
ax4.set_facecolor(background_color)
ax5.set_facecolor(background_color)
ax6.set_facecolor(background_color)
ax7.set_facecolor(background_color)
ax8.set_facecolor(background_color)
# Title of the plot
ax0.spines["bottom"].set_visible(False)
ax0.spines["left"].set_visible(False)
ax0.spines["top"].set_visible(False)
ax0.spines["right"].set_visible(False)
ax0.tick_params(left=False, bottom=False)
ax0.set_xticklabels([])
ax0.set_yticklabels([])
ax0.text(0.5,0.5,
'Count plot for various\n categorical features\n_________________',
horizontalalignment='center',
verticalalignment='center',
fontsize=18, fontweight='bold',
fontfamily='serif',
color="#000000")
# Sex count
ax1.text(0.3, 220, 'Sex', fontsize=14, fontweight='bold', fontfamily='serif', color="#000000")
ax1.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.countplot(ax=ax1,data=df,x='sex',palette=color_palette)
ax1.set_xlabel("")
ax1.set_ylabel("")
# Exng count
ax2.text(0.3, 220, 'Exng', fontsize=14, fontweight='bold', fontfamily='serif', color="#000000")
ax2.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.countplot(ax=ax2,data=df,x='exng',palette=color_palette)
ax2.set_xlabel("")
ax2.set_ylabel("")
# Caa count
ax3.text(1.5, 200, 'Caa', fontsize=14, fontweight='bold', fontfamily='serif', color="#000000")
ax3.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.countplot(ax=ax3,data=df,x='caa',palette=color_palette)
ax3.set_xlabel("")
ax3.set_ylabel("")
# Cp count
ax4.text(1.5, 162, 'Cp', fontsize=14, fontweight='bold', fontfamily='serif', color="#000000")
ax4.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.countplot(ax=ax4,data=df,x='cp',palette=color_palette)
ax4.set_xlabel("")
ax4.set_ylabel("")
# Fbs count
ax5.text(0.5, 290, 'Fbs', fontsize=14, fontweight='bold', fontfamily='serif', color="#000000")
ax5.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.countplot(ax=ax5,data=df,x='fbs',palette=color_palette)
ax5.set_xlabel("")
ax5.set_ylabel("")
# Restecg count
ax6.text(0.75, 165, 'Restecg', fontsize=14, fontweight='bold', fontfamily='serif', color="#000000")
ax6.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.countplot(ax=ax6,data=df,x='restecg',palette=color_palette)
ax6.set_xlabel("")
ax6.set_ylabel("")
# Slp count
ax7.text(0.85, 155, 'Slp', fontsize=14, fontweight='bold', fontfamily='serif', color="#000000")
ax7.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.countplot(ax=ax7,data=df,x='slp',palette=color_palette)
ax7.set_xlabel("")
ax7.set_ylabel("")
# Thall count
ax8.text(1.2, 180, 'Thall', fontsize=14, fontweight='bold', fontfamily='serif', color="#000000")
ax8.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.countplot(ax=ax8,data=df,x='thall',palette=color_palette)
ax8.set_xlabel("")
ax8.set_ylabel("")
for s in ["top","right","left"]:
ax1.spines[s].set_visible(False)
ax2.spines[s].set_visible(False)
ax3.spines[s].set_visible(False)
ax4.spines[s].set_visible(False)
ax5.spines[s].set_visible(False)
ax6.spines[s].set_visible(False)
ax7.spines[s].set_visible(False)
ax8.spines[s].set_visible(False)
fig = plt.figure(figsize=(18,16))
gs = fig.add_gridspec(2,3)
gs.update(wspace=0.3, hspace=0.15)
ax0 = fig.add_subplot(gs[0,0])
ax1 = fig.add_subplot(gs[0,1])
ax2 = fig.add_subplot(gs[0,2])
ax3 = fig.add_subplot(gs[1,0])
ax4 = fig.add_subplot(gs[1,1])
ax5 = fig.add_subplot(gs[1,2])
background_color = "#ffffff"
color_palette = ["#D04848","#F3B95F","#FDE767","#6895D2","#da8829"]
fig.patch.set_facecolor(background_color)
ax0.set_facecolor(background_color)
ax1.set_facecolor(background_color)
ax2.set_facecolor(background_color)
ax3.set_facecolor(background_color)
ax4.set_facecolor(background_color)
ax5.set_facecolor(background_color)
# Title of the plot
ax0.spines["bottom"].set_visible(False)
ax0.spines["left"].set_visible(False)
ax0.spines["top"].set_visible(False)
ax0.spines["right"].set_visible(False)
ax0.tick_params(left=False, bottom=False)
ax0.set_xticklabels([])
ax0.set_yticklabels([])
ax0.text(0.5,0.5,
'Boxen plot for various\n continuous features\n_________________',
horizontalalignment='center',
verticalalignment='center',
fontsize=18, fontweight='bold',
fontfamily='serif',
color="#000000")
# Age
ax1.text(-0.05, 81, 'Age', fontsize=14, fontweight='bold', fontfamily='serif', color="#000000")
ax1.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.boxenplot(ax=ax1,y=df['age'],palette=["#800000"],width=0.6)
ax1.set_xlabel("")
ax1.set_ylabel("")
# Trtbps
ax2.text(-0.05, 208, 'Trtbps', fontsize=14, fontweight='bold', fontfamily='serif', color="#000000")
ax2.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.boxenplot(ax=ax2,y=df['trtbps'],palette=["#8000ff"],width=0.6)
ax2.set_xlabel("")
ax2.set_ylabel("")
# Chol
ax3.text(-0.05, 600, 'Chol', fontsize=14, fontweight='bold', fontfamily='serif', color="#000000")
ax3.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.boxenplot(ax=ax3,y=df['chol'],palette=["#6aac90"],width=0.6)
ax3.set_xlabel("")
ax3.set_ylabel("")
# Thalachh
ax4.text(-0.09, 210, 'Thalachh', fontsize=14, fontweight='bold', fontfamily='serif', color="#000000")
ax4.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.boxenplot(ax=ax4,y=df['thalachh'],palette=["#5833ff"],width=0.6)
ax4.set_xlabel("")
ax4.set_ylabel("")
# oldpeak
ax5.text(-0.1, 6.6, 'Oldpeak', fontsize=14, fontweight='bold', fontfamily='serif', color="#000000")
ax5.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.boxenplot(ax=ax5,y=df['oldpeak'],palette=["#da8829"],width=0.6)
ax5.set_xlabel("")
ax5.set_ylabel("")
for s in ["top","right","left"]:
ax1.spines[s].set_visible(False)
ax2.spines[s].set_visible(False)
ax3.spines[s].set_visible(False)
ax4.spines[s].set_visible(False)
ax5.spines[s].set_visible(False)
fig = plt.figure(figsize=(18,7))
gs = fig.add_gridspec(1,2)
gs.update(wspace=0.3, hspace=0.15)
ax0 = fig.add_subplot(gs[0,0])
ax1 = fig.add_subplot(gs[0,1])
background_color = "#ffffff"
color_palette = ["#D04848","#F3B95F","#FDE767","#6895D2","#da8829"]
fig.patch.set_facecolor(background_color)
ax0.set_facecolor(background_color)
ax1.set_facecolor(background_color)
# Title of the plot
ax0.text(0.5,0.5,"Count of the target\n___________",
horizontalalignment = 'center',
verticalalignment = 'center',
fontsize = 18,
fontweight='bold',
fontfamily='serif',
color='#000000')
ax0.set_xticklabels([])
ax0.set_yticklabels([])
ax0.tick_params(left=False, bottom=False)
# Target Count
ax1.text(0.35,177,"Output",fontsize=14, fontweight='bold', fontfamily='serif', color="#000000")
ax1.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.countplot(ax=ax1, data=df, x = 'output',palette = color_palette)
ax1.set_xlabel("")
ax1.set_ylabel("")
ax1.set_xticklabels(["Low chances of attack(0)","High chances of attack(1)"])
ax0.spines["top"].set_visible(False)
ax0.spines["left"].set_visible(False)
ax0.spines["bottom"].set_visible(False)
ax0.spines["right"].set_visible(False)
ax1.spines["top"].set_visible(False)
ax1.spines["left"].set_visible(False)
ax1.spines["right"].set_visible(False)
# 연속형 변수의 상관계수를 구하고 전치 +) cov -> 공분산, corr -> 상관계수
df_corr = df[con_cols].corr().transpose()
# 출력
df_corr
fig = plt.figure(figsize=(10,10))
gs = fig.add_gridspec(1,1)
gs.update(wspace=0.3, hspace=0.15)
ax0 = fig.add_subplot(gs[0,0])
color_palette = ["#5833ff","#da8829"]
# 상삼각행렬의 원소만 남길 수 있는 np.triu, ones_like는 그대로 복사하는 메소드
mask = np.triu(np.ones_like(df_corr))
ax0.text(1.5,-0.1,"Correlation Matrix",fontsize=22, fontweight='bold', fontfamily='serif', color="#000000")
# 연속형 변수의 상관계수를 구하고 전치
df_corr = df[con_cols].corr().transpose()
// 히트맵: 색상으로 자료의 많고 적음을 나타낼 수 있는 도표
sns.heatmap(df_corr,mask=mask,fmt=".1f",annot=True,cmap='YlGnBu')
plt.show()
fig = plt.figure(figsize=(12,12))
# 상관계수의 스택화 후 인덱스 초기화
corr_mat = df.corr().stack().reset_index(name="correlation")
# 복잡할 수 있지만, replot을 활용하면 산점도와 선그래프를 동시에 그릴 수 있다.
g = sns.relplot(
data=corr_mat,
x="level_0", y="level_1", hue="correlation", size="correlation",
palette="YlGnBu", hue_norm=(-1, 1), edgecolor=".7",
height=10, sizes=(50, 250), size_norm=(-.2, .8),
)
# 플롯 관련 설정들...
g.set(xlabel="features on X", ylabel="featurs on Y", aspect="equal")
g.fig.suptitle('Scatterplot heatmap',fontsize=22, fontweight='bold', fontfamily='serif', color="#000000")
g.despine(left=True, bottom=True)
g.ax.margins(.02)
for label in g.ax.get_xticklabels():
label.set_rotation(90)
for artist in g.legend.legendHandles:
artist.set_edgecolor(".7")
plt.show()
fig = plt.figure(figsize=(18,18))
gs = fig.add_gridspec(5,2)
gs.update(wspace=0.5, hspace=0.5)
ax0 = fig.add_subplot(gs[0,0])
ax1 = fig.add_subplot(gs[0,1])
ax2 = fig.add_subplot(gs[1,0])
ax3 = fig.add_subplot(gs[1,1])
ax4 = fig.add_subplot(gs[2,0])
ax5 = fig.add_subplot(gs[2,1])
ax6 = fig.add_subplot(gs[3,0])
ax7 = fig.add_subplot(gs[3,1])
ax8 = fig.add_subplot(gs[4,0])
ax9 = fig.add_subplot(gs[4,1])
background_color = "#ffffff"
color_palette = ["#D04848","#F3B95F","#FDE767","#6895D2","#da8829"]
fig.patch.set_facecolor(background_color)
ax0.set_facecolor(background_color)
ax1.set_facecolor(background_color)
ax2.set_facecolor(background_color)
ax3.set_facecolor(background_color)
ax4.set_facecolor(background_color)
ax5.set_facecolor(background_color)
ax6.set_facecolor(background_color)
ax7.set_facecolor(background_color)
ax8.set_facecolor(background_color)
ax9.set_facecolor(background_color)
# Age title
ax0.text(0.5,0.5,"Distribution of age\naccording to\n target variable\n___________",
horizontalalignment = 'center',
verticalalignment = 'center',
fontsize = 18,
fontweight='bold',
fontfamily='serif',
color='#000000')
ax0.spines["bottom"].set_visible(False)
ax0.set_xticklabels([])
ax0.set_yticklabels([])
ax0.tick_params(left=False, bottom=False)
# Age
ax1.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.kdeplot(ax=ax1, data=df, x='age',hue="output", fill=True,palette=["#8000ff","#da8829"], alpha=.5, linewidth=0)
ax1.set_xlabel("")
ax1.set_ylabel("")
# TrTbps title
ax2.text(0.5,0.5,"Distribution of trtbps\naccording to\n target variable\n___________",
horizontalalignment = 'center',
verticalalignment = 'center',
fontsize = 18,
fontweight='bold',
fontfamily='serif',
color='#000000')
ax2.spines["bottom"].set_visible(False)
ax2.set_xticklabels([])
ax2.set_yticklabels([])
ax2.tick_params(left=False, bottom=False)
# TrTbps
ax3.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.kdeplot(ax=ax3, data=df, x='trtbps',hue="output", fill=True,palette=["#8000ff","#da8829"], alpha=.5, linewidth=0)
ax3.set_xlabel("")
ax3.set_ylabel("")
# Chol title
ax4.text(0.5,0.5,"Distribution of chol\naccording to\n target variable\n___________",
horizontalalignment = 'center',
verticalalignment = 'center',
fontsize = 18,
fontweight='bold',
fontfamily='serif',
color='#000000')
ax4.spines["bottom"].set_visible(False)
ax4.set_xticklabels([])
ax4.set_yticklabels([])
ax4.tick_params(left=False, bottom=False)
# Chol
ax5.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.kdeplot(ax=ax5, data=df, x='chol',hue="output", fill=True,palette=["#8000ff","#da8829"], alpha=.5, linewidth=0)
ax5.set_xlabel("")
ax5.set_ylabel("")
# Thalachh title
ax6.text(0.5,0.5,"Distribution of thalachh\naccording to\n target variable\n___________",
horizontalalignment = 'center',
verticalalignment = 'center',
fontsize = 18,
fontweight='bold',
fontfamily='serif',
color='#000000')
ax6.spines["bottom"].set_visible(False)
ax6.set_xticklabels([])
ax6.set_yticklabels([])
ax6.tick_params(left=False, bottom=False)
# Thalachh
ax7.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.kdeplot(ax=ax7, data=df, x='thalachh',hue="output", fill=True,palette=["#8000ff","#da8829"], alpha=.5, linewidth=0)
ax7.set_xlabel("")
ax7.set_ylabel("")
# Oldpeak title
ax8.text(0.5,0.5,"Distribution of oldpeak\naccording to\n target variable\n___________",
horizontalalignment = 'center',
verticalalignment = 'center',
fontsize = 18,
fontweight='bold',
fontfamily='serif',
color='#000000')
ax8.spines["bottom"].set_visible(False)
ax8.set_xticklabels([])
ax8.set_yticklabels([])
ax8.tick_params(left=False, bottom=False)
# Oldpeak
ax9.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.kdeplot(ax=ax9, data=df, x='oldpeak',hue="output", fill=True,palette=["#8000ff","#da8829"], alpha=.5, linewidth=0)
ax9.set_xlabel("")
ax9.set_ylabel("")
for i in ["top","left","right"]:
ax0.spines[i].set_visible(False)
ax1.spines[i].set_visible(False)
ax2.spines[i].set_visible(False)
ax3.spines[i].set_visible(False)
ax4.spines[i].set_visible(False)
ax5.spines[i].set_visible(False)
ax6.spines[i].set_visible(False)
ax7.spines[i].set_visible(False)
ax8.spines[i].set_visible(False)
ax9.spines[i].set_visible(False)
fig = plt.figure(figsize=(18,20))
gs = fig.add_gridspec(6,2)
gs.update(wspace=0.5, hspace=0.5)
ax0 = fig.add_subplot(gs[0,0])
ax1 = fig.add_subplot(gs[0,1])
ax2 = fig.add_subplot(gs[1,0])
ax3 = fig.add_subplot(gs[1,1])
ax4 = fig.add_subplot(gs[2,0])
ax5 = fig.add_subplot(gs[2,1])
ax6 = fig.add_subplot(gs[3,0])
ax7 = fig.add_subplot(gs[3,1])
ax8 = fig.add_subplot(gs[4,0])
ax9 = fig.add_subplot(gs[4,1])
ax10 = fig.add_subplot(gs[5,0])
ax11 = fig.add_subplot(gs[5,1])
background_color = "#ffffff"
color_palette = ["#D04848","#F3B95F","#FDE767","#6895D2","#da8829"]
fig.patch.set_facecolor(background_color)
ax0.set_facecolor(background_color)
ax1.set_facecolor(background_color)
ax2.set_facecolor(background_color)
ax3.set_facecolor(background_color)
ax4.set_facecolor(background_color)
ax5.set_facecolor(background_color)
ax6.set_facecolor(background_color)
ax7.set_facecolor(background_color)
ax8.set_facecolor(background_color)
ax9.set_facecolor(background_color)
ax10.set_facecolor(background_color)
ax11.set_facecolor(background_color)
# Cp title
# 0 = Typical Angina, 1 = Atypical Angina, 2 = Non-anginal Pain, 3 = Asymptomatic
ax0.text(0.5,0.5,"Chest pain\ndistribution\n__________",
horizontalalignment = 'center',
verticalalignment = 'center',
fontsize = 18,
fontweight='bold',
fontfamily='serif',
color='#000000')
ax0.spines["bottom"].set_visible(False)
ax0.set_xticklabels([])
ax0.set_yticklabels([])
ax0.tick_params(left=False, bottom=False)
ax0.text(1,.5,"0 - Typical Angina\n1 - Atypical Angina\n2 - Non-anginal Pain\n3 - Asymptomatic",
horizontalalignment = 'center',
verticalalignment = 'center',
fontsize = 14
)
# Cp
ax1.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.kdeplot(ax=ax1, data=df, x='cp',hue="output", fill=True,palette=["#8000ff","#da8829"], alpha=.5, linewidth=0)
ax1.set_xlabel("")
ax1.set_ylabel("")
# Caa title
ax2.text(0.5,0.5,"Number of\nmajor vessels\n___________",
horizontalalignment = 'center',
verticalalignment = 'center',
fontsize = 18,
fontweight='bold',
fontfamily='serif',
color='#000000')
ax2.text(1,.5,"0 vessels\n1 vessel\n2 vessels\n3 vessels\n4vessels",
horizontalalignment = 'center',
verticalalignment = 'center',
fontsize = 14
)
ax2.spines["bottom"].set_visible(False)
ax2.set_xticklabels([])
ax2.set_yticklabels([])
ax2.tick_params(left=False, bottom=False)
# Caa
ax3.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.kdeplot(ax=ax3, data=df, x='caa',hue="output", fill=True,palette=["#8000ff","#da8829"], alpha=.5, linewidth=0)
ax3.set_xlabel("")
ax3.set_ylabel("")
# Sex title
ax4.text(0.5,0.5,"Heart Attack\naccording to\nsex\n______",
horizontalalignment = 'center',
verticalalignment = 'center',
fontsize = 18,
fontweight='bold',
fontfamily='serif',
color='#000000')
ax4.text(1,.5,"0 - Female\n1 - Male",
horizontalalignment = 'center',
verticalalignment = 'center',
fontsize = 14
)
ax4.spines["bottom"].set_visible(False)
ax4.set_xticklabels([])
ax4.set_yticklabels([])
ax4.tick_params(left=False, bottom=False)
# Sex
ax5.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.countplot(ax=ax5,data=df,x='sex',palette=["#8000ff","#da8829"], hue='output')
ax5.set_xlabel("")
ax5.set_ylabel("")
# Thall title
ax6.text(0.5,0.5,"Distribution of thall\naccording to\n target variable\n___________",
horizontalalignment = 'center',
verticalalignment = 'center',
fontsize = 18,
fontweight='bold',
fontfamily='serif',
color='#000000')
ax6.text(1,.5,"Thalium Stress\nTest Result\n0, 1, 2, 3",
horizontalalignment = 'center',
verticalalignment = 'center',
fontsize = 14
)
ax6.spines["bottom"].set_visible(False)
ax6.set_xticklabels([])
ax6.set_yticklabels([])
ax6.tick_params(left=False, bottom=False)
# Thall
ax7.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.kdeplot(ax=ax7, data=df, x='thall',hue="output", fill=True,palette=["#8000ff","#da8829"], alpha=.5, linewidth=0)
ax7.set_xlabel("")
ax7.set_ylabel("")
# Thalachh title
ax8.text(0.5,0.5,"Boxen plot of\nthalachh wrt\noutcome\n_______",
horizontalalignment = 'center',
verticalalignment = 'center',
fontsize = 18,
fontweight='bold',
fontfamily='serif',
color='#000000')
ax8.text(1,.5,"Maximum heart\nrate achieved",
horizontalalignment = 'center',
verticalalignment = 'center',
fontsize = 14
)
ax8.spines["bottom"].set_visible(False)
ax8.set_xticklabels([])
ax8.set_yticklabels([])
ax8.tick_params(left=False, bottom=False)
# Thalachh
ax9.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.boxenplot(ax=ax9, data=df,x='output',y='thalachh',palette=["#8000ff","#da8829"])
ax9.set_xlabel("")
ax9.set_ylabel("")
# Exng title
ax10.text(0.5,0.5,"Strip Plot of\nexng vs age\n______",
horizontalalignment = 'center',
verticalalignment = 'center',
fontsize = 18,
fontweight='bold',
fontfamily='serif',
color='#000000')
ax10.text(1,.5,"Exercise induced\nangina\n0 - No\n1 - Yes",
horizontalalignment = 'center',
verticalalignment = 'center',
fontsize = 14
)
ax10.spines["bottom"].set_visible(False)
ax10.set_xticklabels([])
ax10.set_yticklabels([])
ax10.tick_params(left=False, bottom=False)
# Exng
ax11.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
# 범주형 데이터의 분포
sns.stripplot(ax=ax11, data=df,x='exng',y='age',hue='output',palette=["#8000ff","#da8829"])
ax9.set_xlabel("")
ax9.set_ylabel("")
for i in ["top","left","right"]:
ax0.spines[i].set_visible(False)
ax1.spines[i].set_visible(False)
ax2.spines[i].set_visible(False)
ax3.spines[i].set_visible(False)
ax4.spines[i].set_visible(False)
ax5.spines[i].set_visible(False)
ax6.spines[i].set_visible(False)
ax7.spines[i].set_visible(False)
ax8.spines[i].set_visible(False)
ax9.spines[i].set_visible(False)
ax10.spines[i].set_visible(False)
ax11.spines[i].set_visible(False)
sns.pairplot(df,hue='output',palette = ["#8000ff","#da8829"])
plt.show()
4. 데이터셋 전처리
4.1 탐색적 데이터 분석 결과
- 결측값은 없다.
- 모든 연속형 변수에는 확실히 이상치가 있다.
- sex가 0인 사람보다 sex가 1인 사람의 수가 2배 이상으로 구성되어 있다.
- 히트맵에 따르면 연속형 변수의 선형성은 나타나지 않았다.
- 산점도, 히트맵 행렬은 output과 cp, thalachh, slp 사이에 아마도 상관관계가 있다는 것을 암시한다.
- 직관적으로 나이가 많은 사람들은 심장병을 위험이 높았고, 그에 따라 age, output, wrt
우원 /
안녕하세요👏
우원입니다.
우원입니다.
