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| import streamlit as st | |
| import pandas as pd | |
| import seaborn as sns | |
| import matplotlib.pyplot as plt | |
| import plotly.express as px | |
| from PIL import Image | |
| st.set_page_config( | |
| page_title='EDA', | |
| layout='wide', | |
| initial_sidebar_state='expanded' | |
| ) | |
| def run(): | |
| # title | |
| st.title('Predicting Patient Survival') | |
| # sub header | |
| st.subheader('Understanding Mortality Risk Factors and Predictive Modeling') | |
| # add pic | |
| image = Image.open('heart.png') | |
| st.image(image, caption='Health Parameter') | |
| # add text | |
| ''' | |
| Page ini dibuat untuk memprediksi **kematian pasien** dengan menggunakan | |
| dataset yang tersedia. Dengan melakukan explorasi, akan ditinjau beberapa | |
| insight yang dapat menjadi faktor-faktor / variabel yang membuat seorang | |
| pasien dapat diprediksi meninggal atau tidak. | |
| ''' | |
| markdown_text = ''' | |
| ## Variable Descriptions | |
| | Variable | Description | | |
| |-------------------------|-----------------------------------------------------------------------------------------------| | |
| | age | Age of the patient in years. | | |
| | anaemia | Whether the patient has anemia or not. (1=True, 0=False) | | |
| | creatinine_phosphokinase| Level of the enzyme creatinine phosphokinase in the patient's blood in units (mcg/L). | | |
| | diabetes | Whether the patient has diabetes or not. (1=True, 0=False) | | |
| | ejection_fraction | Percentage of blood volume ejected from the heart during each contraction. | | |
| | high_blood_pressure | Whether the patient has high blood pressure or not. (1=True, 0=False) | | |
| | platelets | Level of platelets in the patient's blood in units (kiloplatelets/mL). | | |
| | serum_creatinine | Level of creatinine in the patient's blood in units (mg/dL). | | |
| | serum_sodium | Level of sodium in the patient's blood in units (mEq/L). | | |
| | sex | Gender of the patient. (0=Male, 1=Female) | | |
| | smoking | Whether the patient smokes or not. (1=True, 0=False) | | |
| | time | Follow-up period in days. | | |
| | DEATH_EVENT | Indicator of whether the patient experienced a death event or not. (1=True, 0=False) | | |
| ''' | |
| st.markdown(markdown_text) | |
| st.markdown('---') | |
| st.subheader('Explorasi Data') | |
| st.markdown('---') | |
| st.write('### Tabel Data Pasien') | |
| # show dataframe | |
| data = pd.read_csv('h8dsft_P1G3_Andikaa_Atmanegara_Putra.csv') | |
| st.dataframe(data) | |
| st.markdown('---') | |
| # visual barplot | |
| st.write('### Histogram Berdasarkan User Input ') | |
| choice = st.selectbox('Pilih Columns: ', ('age', 'anaemia', | |
| 'sex', 'DEATH_EVENT', | |
| 'creatinine_phosphokinase', | |
| 'platelets')) | |
| fig = plt.figure(figsize=(15,5)) | |
| sns.histplot(data[choice], bins=30, kde=True) | |
| st.pyplot(fig) | |
| # visual 2 | |
| ## Categorical Data Plot | |
| st.write('### Categorical Data Ratio') | |
| pilihan_kategori = st.selectbox('Pick Categorical Column : ', ('anaemia', 'diabetes', 'high_blood_pressure', | |
| 'sex', 'smoking')) | |
| fig= plt.figure(figsize=(8, 6)) | |
| sns.countplot(data=data, x=pilihan_kategori, hue='DEATH_EVENT', palette=['limegreen', 'firebrick']) | |
| plt.xlabel(pilihan_kategori.capitalize()) | |
| plt.ylabel('Count') | |
| plt.title(pilihan_kategori.capitalize()+' Ratio') | |
| plt.legend(title='DEATH_EVENT') | |
| st.pyplot(fig) | |
| if __name__ == '__main__': | |
| run() |