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import streamlit as st
import pandas as pd
import joblib
st.sidebar.header('Grade Challange 3')
st.sidebar.write("""
Created by Wawan Setiawan S
Use the sidebar to select input features.
""")
@st.cache
def fetch_data():
df = pd.read_csv('h8dsft_P1G3_Wawan_Setiawan.csv')
df['anaemia'] = df['anaemia'].replace({0: 'no', 1 : 'yes'})
df['diabetes'] = df['diabetes'].replace({0 : 'no', 1: 'yes'})
df['high_blood_pressure'] = df['high_blood_pressure'].replace({0 : 'no', 1: 'yes'})
df['smoking'] = df['smoking'].replace({0 : 'no', 1: 'yes'})
df['sex'] = df['sex'].replace({0 : 'female', 1: 'male'})
df['DEATH_EVENT'] = df['DEATH_EVENT'].astype(float)
return df
df = fetch_data()
creatinine_phosphokinase = st.sidebar.slider('creatinine_phosphokinase', 30,8000)
serum_creatinine = st.sidebar.slider('serum_creatinine', 0.0,10.0)
serum_sodium = st.sidebar.slider('serum_sodium', 100., 150.)
age = st.sidebar.slider('age', 1,100)
time = st.sidebar.slider('time', 1.0, 300.0)
smoking = st.sidebar.selectbox('smoking',['yes','no'])
data = {
'creatinine_phosphokinase': creatinine_phosphokinase,
'serum_creatinine': serum_creatinine,
'serum_sodium': serum_sodium,
'age': age,
'time': time,
'smoking':smoking
}
input = pd.DataFrame(data, index=[0])
st.subheader('User Input')
st.write(input)
load_model = joblib.load("all_process.pkl")
if st.button('Predict'):
prediction = load_model.predict(input)
if prediction == 1:
prediction = 'Yes'
else:
prediction = 'No'
st.write('Based on user input, the placement model predicted: ')
st.write(prediction) |