Upload 6 files

app.py

@@ -0,0 +1,10 @@
+import streamlit as st
+import eda
+import prediction
+
+navigation = st.sidebar.selectbox('Pilih Halaman : ', ('EDA','Predict Heart Failure'))
+
+if navigation == 'EDA':
+    eda.run()
+else:
+    prediction.run()

eda.py

@@ -0,0 +1,69 @@
+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('Heart Rate Survivor Prediction')
+
+    # Membuat Sub Header
+    st.subheader('EDA untuk Analisa Dataset Heart Failure')
+
+    # Menambahkan Gambar
+    image = Image.open('heart.jpg')
+    st.image(image, caption='Love your Heart')
+
+    # Menambahkan Deskripsi
+    st.write('Page ini dibuat oleh **Satriya Fauzan Adhim**')
+
+    # Membuat Garis Lurus
+    st.markdown('---')
+
+    # Magic Syntax
+    '''
+    Pada page ini, penulis akan melakukan eksplorasi sederhana dari dataset Heart Failure.
+    Dataset yang digunakan adalah dataset yang berisikan tentang data terkait dengan heart failure.
+    Dataset diambil dari Google Bigquery.
+
+    '''
+
+    # Show DataFrame
+    data = pd.read_csv('https://raw.githubusercontent.com/kodokgodog/Latihan_hactiv8/main/h8dsft_P1G3_Satriya_Fauzan_Adhim.csv')
+    st.dataframe(data)
+
+    # Membuat Barplot
+    st.write('#### Plot DEATH_EVENT')
+    fig = plt.figure(figsize=(15, 5))
+    sns.countplot(x='DEATH_EVENT', data=data)
+    st.pyplot(fig)
+
+    # Membuat Historgram
+    st.write('#### Histogram of ejection_fraction')
+    fig = plt.figure(figsize=(15, 5))
+    sns.histplot(data['ejection_fraction'], bins=30, kde=True)
+    st.pyplot(fig)
+
+    # Membuat Histogram Berdasarkan Input User
+    st.write('#### Histogram berdasarkan input user')
+    pilihan = st.selectbox('Pilih column : ', ('age', 'ejection_fraction', 'serum_creatinine', 'serum_sodium'))
+    fig = plt.figure(figsize=(15, 5))
+    sns.histplot(data[pilihan], bins=30, kde=True)
+    st.pyplot(fig)
+
+    # Membuat Plotly Plot
+    st.write('#### Plotly Plot - anaemia dengan DEATH_EVENT')
+    fig = px.scatter(data, x='anaemia', y='DEATH_EVENT', hover_data=['age'])
+    st.plotly_chart(fig)
+
+if __name__== '__main__':
+    run()

list_num_cols.txt

@@ -0,0 +1 @@
+["age", "ejection_fraction", "serum_creatinine", "serum_sodium", "time"]

model_rf.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:78d62464a52df0bf9d4b0a9f91aa35e843bcbd512bb03a196f7a1f7401ef61ce
+size 486809

model_scaler.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9e9f35bfb5b324f99cb603976c6579c62b40f686098f9371558df3e8f3378c9e
+size 1247

prediction.py

@@ -0,0 +1,75 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import json
+import joblib
+
+# load file joblib dengan .pkl
+with open('model_rf.pkl', 'rb') as file_1:
+  model_rf= joblib.load(file_1)
+
+with open('model_scaler.pkl', 'rb') as file_2:
+  model_scaler= joblib.load(file_2)
+
+#loan file txt dengan json.load
+with open('list_num_cols.txt', 'r') as file_3:
+  num_columns= json.load(file_3)
+
+def run() :
+
+  with st.form(key='from_heart_failure'):
+      #name = st.text_input('Full Name', value='')
+      age = st.number_input('Age', min_value=25, max_value=100, value=25, step=1, help='Usia Pasien')
+      sex = st.selectbox('Sex', ('Man', 'Woman'), index=1)
+      smoking = st.selectbox('Smoking', ('No', 'Yes'), index=1)
+      st.markdown('---')
+      
+      phosphokinase = st.slider('Creatinine Phosphokinase', min_value=23, max_value=7861, value=70)
+      fraction = st.slider('Ejection Fraction', 10, 90, 50)
+      platelets = st.slider('Platelets', 25000, 900000, 125000)
+      creatinine = st.number_input('Serum Creatinine', min_value=0.0, max_value=10.0, value=0.0, step=0.1)
+      sodium = st.slider('Serum Sodium', 100, 148, 120)
+      time = st.slider('Time', 1, 300, 1)
+      st.markdown('---')
+
+      anaemia = st.selectbox('Have Anaemia', ('No', 'Yes'), index=1)
+      diabetes = st.selectbox('Have Diabetes', ('No', 'Yes'), index=1)
+      high_blood_pressure = st.selectbox('Have High Blood Pressure', ('No', 'Yes'), index=1)
+      DEATH_EVENT = st.selectbox('Death Event', ('Survived', 'Dead'), index=1)
+
+      submitted = st.form_submit_button('Predict')
+
+  data_inf = {
+      'age': age,
+      'anaemia': anaemia,
+      'creatin_phosphokinase: ': phosphokinase,
+      'diabetes': diabetes,
+      'ejection_fraction': fraction,
+      'high_blood_pressure': high_blood_pressure,
+      'platelets': platelets,
+      'serum_creatinine': creatinine,
+      'serum_sodium': sodium,
+      'sex': sex,
+      'smoking': smoking,
+      'time': time,
+      'DEATH_EVENT': DEATH_EVENT,
+  }
+
+  data_inf = pd.DataFrame([data_inf])
+  data_inf
+
+
+  if submitted:
+    #Numerical Columns
+    data_inf_num = data_inf[num_columns]
+
+    # Feature Scaling
+    data_inf_final = model_scaler.transform(data_inf_num)
+    
+    # Predict using Random Forest
+    y_pred_inf = model_rf.predict(data_inf_final)
+    prediction_label = "Survived" if y_pred_inf == 0 else "Dead"
+    st.write('# Rating : ', prediction_label)
+
+if __name__== '__main__':
+    run()