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app.py

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+import streamlit as st
+import pandas as pd
+import pickle
+import joblib
+
+st.title("Prediction of Death Event")
+
+# import model
+model = pickle.load(open("boosted.pkl", "rb"))
+
+
+
+st.write('Insert feature below to predict')
+
+# user input
+age = st.number_input(label='Age', min_value=40, max_value=95, value=40, step=1)
+anaemia = st.selectbox(label='Anaemia', options=[0,1])
+creatinine_phosphokinase = st.number_input(label='Creatinine Phosphokinase', min_value=23.0, max_value=1954.5, value=23.0, step=0.1)
+diabetes = st.selectbox(label='Diabetes', options=[0,1])
+ejection_fraction = st.number_input(label='Ejection Fraction', min_value=14.0, max_value=73.4, value=14.0, step=0.1)
+high_blood_pressure = st.selectbox(label='High Blood Pressure', options=[0,1])
+platelets = st.number_input(label='Platelets', min_value=25100, max_value=543000, value=26000, step=10)
+serum_creatinine = st.number_input(label='Serum Creatinine', min_value=0.5, max_value=4.2, value=1.5, step=0.1)
+smoking	= st.selectbox(label='Smoking', options=[0,1])
+time = st.number_input(label='Time', min_value=4, max_value=285, value=10, step=1)
+
+# convert into dataframe
+data = pd.DataFrame({'Age': [age],
+                'Anaemia': [anaemia],
+                'Creatinine Phosphokinas': [creatinine_phosphokinase],
+                'Diabetes':[diabetes],
+                'Ejection Fraction': [ejection_fraction],
+                'High Blood Pressure': [high_blood_pressure],
+                'Platelets': [platelets],
+                'Serum Creatinine': [serum_creatinine],
+                'Smoking': [smoking],
+                'Time': [time]})
+
+# model predict
+
+clas = model.predict(data).tolist()[0]
+
+# interpretation
+st.write('Classification Result: ')
+if clas == 1:
+    st.text('Die')
+else:
+    st.text('Alive')

boosted.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6c6e13bdf4dec37d6f4424f1f0f9688c8d813ca28d2fbd5b5b356a6d4a5637ab
+size 3693246