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app.py

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+import streamlit as st
+import eda
+import prediction
+
+navigation = st.sidebar.selectbox('pilih halaman : ', ('EDA', 'Predict of Death'))
+
+if navigation == 'EDA':
+    eda.run()
+
+else:
+    prediction.run()

eda.py

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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
+
+# untuk lebarkan layout setelah import
+st.set_page_config(
+    page_title = 'Hearth Failure',
+    layout = 'wide',
+    initial_sidebar_state='expanded'
+)
+
+def run():
+
+    # Membuat file
+    st.title( 'Death Prediction : Hearth Failure')
+
+    # Membuat sub header
+    st.subheader('EDA Hearth Failure')
+
+    # Menambahkan gambar
+    image = Image.open('hearth attack.jpg')
+    st.image(image, caption='Hearth Failure')
+
+    # Menambahkan deskripsi
+    st.write('Page ini merupakan hasil EDA dari dataset Hearth Failure')
+
+
+    # show data frame
+    df = pd.read_csv('https://raw.githubusercontent.com/mukhlishr/rasyidi/main/h8dsft_P1G3_mukhlish_rasyidi.csv')
+    st.dataframe(df)
+
+    # Membuat Barplot 
+    st.write('###### Plot Death Event')
+    fig=plt.figure(figsize=(15,5))
+    sns.countplot(x='DEATH_EVENT', data = df)
+    st.pyplot(fig)
+
+    # Membuat histogram
+    st.write('##### Histogram of Age')
+    fig = plt.figure(figsize=(15,5))
+    sns.histplot(df['age'], bins=30, kde=True)
+    st.pyplot(fig)
+
+    
+
+if __name__ == '__main__':
+    run()

gbc_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2503e18457402e9f89f3d04f7f92aafba07110c6e24c2a1200cc7d251ea63fe4
+size 117364

prediction.py

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+import streamlit as st
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import pickle
+import json
+
+
+# Load All Files
+
+with open('gbc_model.pkl', 'rb') as file_1:
+  gbc_model = pickle.load(file_1)
+
+with open('scaler.pkl', 'rb') as file_2:
+  scaler = pickle.load(file_2)
+
+
+# bikin fungsi  
+def run():
+
+  with st.form(key='patient_condition'):
+      anemia = st.selectbox('Anemia', (0, 1), index=1)
+      diabetes = st.selectbox('Diabetes', (0, 1), index=1)
+      high_blood_pressure = st.selectbox('High blood pressure', (0, 1), index=1)
+      serum_sodium = st.number_input('Serum Sodium', min_value=100, max_value=150, value=100)
+      sex = st.selectbox('Sex', (0, 1), index=1)
+      smoking = st.selectbox('Sex', (0, 1), index=1)
+      time = st.number_input('Time', min_value=100, max_value=150, value=100)
+      binned_age  = st.selectbox('Range of age', (1, 2,3,4), index=1)
+      binned_creat_serum = st.selectbox('High serum creatinine', (1, 2), index=1)
+      binned_cpk = st.selectbox('CPK Value', (1,2,3), index=1)
+      binned_ejection = st.selectbox(' Ejection frraction', (1,2,3), index=1)
+      binned_cpk = st.selectbox('CPK Value', (1,2,3), index=1)
+      binned_platelets = st.selectbox('Platelets count', (0, 1, 2), index=1)
+      st.markdown('---')
+
+      submitted = st.form_submit_button('Predict')
+
+  data_inf = {
+    'anemia': 1,
+    'diabetes': 1,
+    'high_blood_pressure' : 1,
+    'serum_sodium': 130,
+    'sex': 1,  
+    'smoking': 1,
+    'time': 150,
+    'binned_age': 3,
+    'binned_creat_serum' : 2,
+    'binned_cpk' : 2,
+    'binned_ejection' : 2,
+    'binned_platelets': 2
+  }
+
+  data_inf = pd.DataFrame([data_inf])
+  st.dataframe(data_inf)
+
+  if submitted:
+
+   # Feature Scaling and Feature Encoding
+      col_headers = list(data_inf.columns.values)
+      data_inf_scaled = scaler.transform(data_inf)
+      data_inf_df = pd.DataFrame(data_inf_scaled, columns=col_headers) 
+
+
+      # Predict using gbc
+      y_pred_inf = gbc_model.predict(data_inf_df)
+      st.write('# Rating: ', str(int(y_pred_inf)))
+      
+if __name__ == '__main__':
+    run()

requirements.txt

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+# daftar library yang dibutuhkan semua
+streamlit
+pandas
+seaborn
+matplotlib
+numpy
+scikit-learn==1.2.1
+plotly

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ec5fb5bd0c630066544195baaebb1c4e174fcd7c8eb816c6ad7a5371d8274803
+size 988