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import streamlit as st |
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import pandas as pd |
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import numpy as np |
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import pickle |
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import ast |
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def run(): |
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st.header("Model Prediction") |
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with open('scaler.pkl', 'rb') as file_1: |
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scaler = pickle.load(file_1) |
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with open('model_knn.pkl', 'rb') as file_2: |
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model_knn = pickle.load(file_2) |
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limit_balance = st.number_input(label='Limit balance nasabah') |
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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]) |
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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]) |
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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]) |
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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]) |
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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]) |
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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]) |
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df_inf = pd.DataFrame({ |
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'limit_balance': limit_balance, |
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'pay_1': pay_1, |
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'pay_2': pay_2, |
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'pay_3': pay_3, |
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'pay_4': pay_4, |
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'pay_5': pay_5, |
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'pay_6': pay_6, |
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},index=[0]) |
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st.table(df_inf) |
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if st.button(label='predict'): |
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df_inf_num = df_inf[['limit_balance']] |
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df_inf_cat= df_inf[['pay_1', 'pay_2', 'pay_3', 'pay_4','pay_5','pay_6']] |
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df_inf_num_scaled = scaler.transform(df_inf_num) |
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df_inf_num_scaled=pd.DataFrame(df_inf_num_scaled) |
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df_inf_final = np.concatenate([df_inf_num_scaled,df_inf_cat],axis = 1) |
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y_pred_inf = model_knn.predict(df_inf_final) |
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st.write(y_pred_inf[0]) |
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if y_pred_inf == 0: |
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st.write('Nasabah Terprediksi bisa membayar') |
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else: |
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st.write('Nasabah Terprediksi tidak bisa membayar') |
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