commit1

app.py

@@ -0,0 +1,10 @@
+import streamlit as st
+import eda
+import predict
+
+navigation = st.sidebar.selectbox('pilih halaman :', ('EDA',"Prediction"))
+
+if navigation =='EDA':
+    eda.run()
+else:
+    predict.run()

col_penting.txt

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+["age", "ejection_fraction", "serum_creatinine", "serum_sodium", "time"]

eda.py

@@ -0,0 +1,75 @@
+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 = 'Heart Failure - EDA',
+    layout = 'wide',
+    initial_sidebar_state='expanded'
+)
+
+def run():
+
+
+    # Membuat Title
+    st.title('Graded Challenge 3 Phase 1')
+
+    # Membuat Sub Header
+    st.subheader('EDA untuk Analisa Dataset Heart Failure')
+
+    # Menambahkan Gambar
+    image = Image.open('SystolicHeartFailureHero.jpg')
+    st.image(image, caption='Heart Photo')
+
+    # Menambahkan Deskripsi
+    st.write('Page ini dibuat oleh *William*')
+
+    # Membuat Garis Lurus
+    st.markdown('---')
+
+    # Magic Syntax
+    '''
+    Pada page kali ini, penulis akan melakukan eksplorasi sederhana.
+    Dataset yang digunakan adalah dataset Heart Failure.
+    Dataset ini berasal dari BigQuery Hacktiv8.
+    '''
+
+    # Show DataFrame
+    data = pd.read_csv('h8dsft_P1G3_William.csv')
+    st.dataframe(data)
+
+    st.write('#### Plot Banyaknya pasien yang hidup / meninggal ')
+    fig = plt.figure(figsize=(15, 5))
+    sns.countplot(x='DEATH_EVENT',data=data)
+    st.pyplot(fig)
+
+    '''
+    berdasarkan data, pasien penyakit jantung memiliki kemungkinan untuk selamat lebih besar daripada mengalami kematian
+    '''
+       
+    # Membuat Barplot
+    st.write('#### Plot death event berdasarkan age')
+    fig = plt.figure(figsize=(15, 5))
+    sns.histplot(data[data.DEATH_EVENT == 0].age,kde=False,color="blue", label="Age for Death Event 0")
+    sns.histplot(data[data.DEATH_EVENT == 1].age,kde=False,color = "red", label = "Age for Death Event 1")
+    plt.legend()
+    st.pyplot(fig)
+    
+    '''
+    dapat dilihat bahwa lebih besar kemungkinan pasien yang meninggal pada umur diatas 80an dibandingkan dengan yang umurnya lebih muda
+    ditnjukkan dengan plot berwarna merah yang lebih tinggi pada umur 80 tahun keatas
+    '''
+
+    # Membuat Histogram Berdasarkan Input User
+    st.write('#### Histogram berdasarkan input user')
+    pilihan = st.selectbox('Pilih column : ', ('age', 'anaemia', 'creatinine_phosphokinase', 'diabetes','ejection_fraction','high_blood_pressure','platelets','serum_creatinine','serum_creatinine','sex','smoking','time'))
+    fig = plt.figure(figsize=(15, 5))
+    sns.histplot(data[pilihan], bins=30, kde=True)
+    st.pyplot(fig)
+
+
+if __name__ == '__main__':
+    run()

h8dsft_P1G3_William.csv

@@ -0,0 +1,300 @@
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model_RF.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:84f1496f9109a19f3184787096aaa958e8826aac1c26134c4819e1ce73c3cf4d
+size 544367

model_boost.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ab210b9a8da583c4972afb8063b6a2557165ff63d06b30ed1e3c99f83e5785ec
+size 33786

predict.py

@@ -0,0 +1,65 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+import json
+from joblib import load
+
+# Load All Files
+# Muat model menggunakan joblib
+loaded_model_RF = load('model_RF.joblib')
+loaded_model_boost = load('model_boost.joblib')
+with open('col_penting.txt', 'r') as file_1:
+  col = json.load(file_1)
+
+
+def run():
+  with st.form('key=form_Heart_failure_prediction'):
+      age = st.number_input('Age', min_value=40, max_value=100, value=45, step=1, help='Usia Pasien')
+      anaemia = st.radio('anaemia', (0,1), index=0,help='0 = No, 1 = Yes')
+      creatinine_phosphokinase = st.slider('creatinine phosphokinase', 20, 1500, 200)
+      diabetes = st.radio('diabetes', (0,1), index=1,help='0 = No, 1 = Yes')
+      st.markdown('---')
+
+      ejection_fraction = st.slider('ejection_fraction', 14, 80, 25)
+      high_blood_pressure = st.radio('high blood pressure', (0,1), index=0,help='0 = No, 1 = Yes')
+      platelets = st.slider('platelets', 25000, 850000, 100000)
+      serum_creatinine = st.number_input('serum creatinine', min_value=0.5, max_value=2.5, value=0.75, step=0.05)
+      serum_sodium = st.number_input('serum sodium', min_value=110, max_value=150, value=125, step=1)
+      sex = st.selectbox('sex', (0,1), index=1,help='0 = Female, 1 = Male')
+      smoking = st.selectbox('smoking', (0,1), index=0,help='0 = No, 1 = Yes')
+      time = st.slider('time', 3, 300, 30)
+      model = st.radio('pilih jenis model yang akan digunakan untuk prediksi :',('Random Forest', 'AdaBoost'),index=1)
+
+      submitted = st.form_submit_button('Predict')
+
+  data_inf = {
+      'age': age,
+      'anaemia': anaemia,
+      'creatinine_phosphokinase': creatinine_phosphokinase,
+      'diabetes': diabetes,
+      'ejection_fraction': ejection_fraction,
+      'high_blood_pressure': high_blood_pressure,
+      'platelets': platelets,
+      'serum_creatinine': serum_creatinine,
+      'serum_sodium': serum_sodium,
+      'sex': sex,
+      'smoking': smoking,
+      'time': time
+  }
+  
+  data_inf = pd.DataFrame([data_inf])
+  inf_new = data_inf[col]
+  if model == 'AdaBoost':
+      if submitted: 
+        y_pred_inf = loaded_model_boost.predict(inf_new)
+        st.write('# Death : ', str(int(y_pred_inf)))
+  else :
+     if submitted: 
+        y_pred_inf = loaded_model_RF.predict(inf_new)
+        st.write('# Death : ', str(int(y_pred_inf)))
+
+
+
+if __name__ == '__main__':
+    run()

prep.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d2c5d0df6c953b61e39b183f1373dd3272d9d25ea2b9ef9d6a042c3665a07b35
+size 2150