new

GB.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2744b091607469ff490b0c9e81fbb65bbcc226264d94e417467fd9e1dc382422
+size 11540004

app.py

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+import streamlit as st
+import prediction
+import eda
+
+navigation = st.sidebar.selectbox('Pilih Halaman: ', ('Predict', 'EDA'))
+
+if navigation == 'Predict':
+    prediction.run()
+else:
+    eda.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
+
+st.set_page_config(
+   page_title= 'Prediksi Serangan Jantung',
+   layout='wide',
+   initial_sidebar_state='expanded'
+)
+
+def run():
+    image = Image.open('image.png')
+    resized_image = image.resize((300, 300))
+    st.image(resized_image, caption='Serangan jantung')
+
+    st.title('Prediksi Serangan Jantung')
+    df = pd.read_csv('https://raw.githubusercontent.com/Azrieldr/latihan/master/h8dsft_P1G3_Azrieldr.csv')
+    st.dataframe(df)
+
+
+    persentaseKematian = df['DEATH_EVENT'].mean()*100
+    # Create pie chart
+    fig, ax = plt.subplots(figsize=(10,15), dpi=100)
+    ax.pie([persentaseKematian, 100-persentaseKematian], labels=['Meninggal', 'Selamat'], autopct='%1.1f%%')
+    ax.set_title('Persentase angka kematian')
+    st.pyplot(fig)
+
+    jender = {0: 'Perempuan', 1: 'Laki Laki'}
+    persentaseJender = df.replace({'sex': jender}).groupby('sex')['DEATH_EVENT'].mean()*100
+    st.write('Persentase kematian per jender\n',persentaseJender)
+    deathperjender= df.replace({'sex': jender}).groupby('sex')['DEATH_EVENT'].sum()
+    st.write('Total kematian per jender',deathperjender)
+    BP = {0: 'rendah-normal', 1: 'tinggi'}
+    BPx = df.replace({'high_blood_pressure': BP}).groupby('high_blood_pressure')['DEATH_EVENT'].mean()*100
+    st.write('Total kematian per jender',BPx)
+
+
+
+if __name__== '__main__':
+    run()

prediction.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle 
+
+
+# Load All Files
+with open('rf.pkl', 'rb') as file_1:
+  rf= pickle.load(file_1)
+
+with open('GB.pkl', 'rb') as file_2:
+  GB=pickle.load(file_2)
+
+with open('scaler.pkl', 'rb') as file_3:
+    scaler=pickle.load(file_3)
+
+def run():
+
+    with st.form(key='form_prediksi'):
+        name = st.text_input('Nama', value='')
+        anaemia= st.radio('Apakah memiliki anaemia?', ('Ya', 'Tidak'))
+        if anaemia=='Ya':
+            anaemianum=1
+        else: anaemianum=0
+                
+        age= st.number_input('Umur', min_value=16, max_value=80, value=50, step=1)
+        creatinine_phosphokinase = st.number_input('Creatinine Phosphokinase', min_value=0, max_value=2000, value=1000)
+        diabetes = st.radio('Apakah memiliki Diabetes?', ('Ya', 'Tidak'))
+        if diabetes=='Ya':
+            diabetesnum=1
+        else: diabetesnum=0
+                
+        ejection_fraction = st.number_input('Ejection Fraction', min_value=0, max_value=100, value=50)
+        high_blood_pressure = st.radio('Apakah memiliki darah tinggi?', ('Ya', 'Tidak'))
+        if high_blood_pressure=='Ya':
+            high_blood_pressurenum=1
+        else: high_blood_pressurenum=0
+        
+        platelets = st.number_input('Jumlah trombosit', min_value=0, max_value=300000, value=200000)
+        serum_creatinine = st.number_input('Serum creatinine', min_value=0, max_value=100, value=50)
+        serum_sodium = st.number_input('Serum sodium', min_value=0.0, max_value=2.0, value=0.4, format='%.2f', step=0.1)
+        sex = st.radio('Kelamin', ('Perempuan', 'Laki-Laki'))
+        if sex=='Laki-Laki':
+            sexnum=1
+        else: sexnum=0
+        
+        smoking = st.radio('Apakah merokok?', ('Ya', 'Tidak'))
+        if smoking=='Ya':
+            smokingnum=1
+        else: smokingnum=0
+        
+        time = st.number_input('Waktu', min_value=0, max_value=240, value=120)
+
+        submitted = st.form_submit_button('Predict')
+
+    data_inf = {
+        'age': age,
+        'anaemia': anaemianum,
+        'creatinine_phosphokinase':creatinine_phosphokinase,
+        'diabetes':diabetesnum,
+        'ejection_fraction':ejection_fraction,
+        'high_blood_pressure':high_blood_pressurenum,
+        'platelets': platelets,
+        'serum_creatinine': serum_creatinine,
+        'serum_sodium':serum_sodium,
+        'sex':sexnum,
+        'smoking':smokingnum,
+        'time':time
+    }
+
+    data_inf = pd.DataFrame([data_inf])
+    st.dataframe(data_inf)
+
+    if submitted:
+
+        data_inf_scaled=scaler.transform(data_inf)
+        y_pred_inf_rf = rf.predict(data_inf_scaled)
+        y_pred_inf_GB= GB.predict(data_inf_scaled)
+        st.write('# Prediksi Gradient Boosting: ', str(int(y_pred_inf_GB)))
+        st.write('# Prediski Randomforest: ', str(int(y_pred_inf_rf)))
+
+if __name__== '__main__':
+    run()

requirements.txt

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+streamlit
+pandas
+seaborn
+matplotlib
+plotly
+numpy
+scikit-learn==1.2.1
+Pillow

rf.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a169a590c6e166d06b38b0f1bc27d6ef67a91d0e3a5961abcfc94227a719a059
+size 52783101

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:85c4993c8c580ad9e0d77d4d3ac98922648dd803ec5563043eea4b6c1f68b098
+size 1212