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import streamlit as st
import pandas as pd
import numpy as np
import pickle
import ast
def run():
st.header("Model Prediction")
with open('scaler.pkl', 'rb') as file_1:
scaler = pickle.load(file_1)
with open('model_knn.pkl', 'rb') as file_2:
model_knn = pickle.load(file_2)
limit_balance = st.number_input(label='Limit balance nasabah')
pay_1 = st.selectbox(label='Delay Payment on September 2015',options=[-2.0,-1.0,0.0,1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0])
pay_2 = st.selectbox(label='Delay Payment on Agustus 2015',options=[-2.0,-1.0,0.0,1.0,2.0,3.0,4.0,5.0,6.0,7.0])
pay_3 = st.selectbox(label='Delay Payment on Juli 2015',options=[-2.0,-1.0,0.0,2.0,3.0,4.0,5.0,6.0,7.0])
pay_4 = st.selectbox(label='Delay Payment on Juni 2015',options=[-2.0,-1.0,0.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0])
pay_5 = st.selectbox(label='Delay Payment on May 2015',options=[-2.0,-1.0,0.0,2.0,3.0,4.0,5.0,6.0,7.0])
pay_6 = st.selectbox(label='Delay Payment on April 2015',options=[-2.0,-1.0,0.0,2.0,3.0,4.0,6.0,7.0])
df_inf = pd.DataFrame({
'limit_balance': limit_balance,
'pay_1': pay_1,
'pay_2': pay_2,
'pay_3': pay_3,
'pay_4': pay_4,
'pay_5': pay_5,
'pay_6': pay_6,
},index=[0])
st.table(df_inf)
if st.button(label='predict'):
# define data bedasarkan numerik dan kategori
df_inf_num = df_inf[['limit_balance']]
df_inf_cat= df_inf[['pay_1', 'pay_2', 'pay_3', 'pay_4','pay_5','pay_6']]
df_inf_num_scaled = scaler.transform(df_inf_num)
df_inf_num_scaled=pd.DataFrame(df_inf_num_scaled)
df_inf_final = np.concatenate([df_inf_num_scaled,df_inf_cat],axis = 1)
y_pred_inf = model_knn.predict(df_inf_final)
st.write(y_pred_inf[0])
if y_pred_inf == 0:
st.write('Nasabah Terprediksi bisa membayar')
else:
st.write('Nasabah Terprediksi tidak bisa membayar')
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