Upload 2 files

app.py

@@ -2,7 +2,7 @@ import streamlit as st
 import eda
 import prediction
 
-page = st.sidebar.selectbox('Pilih Halaman: ', ('EDA', 'Prediction'))
+page = st.sidebar.selectbox('Select Page: ', ('EDA', 'Prediction'))
 
 if page == 'EDA':
     eda.app()

prediction.py

@@ -16,8 +16,8 @@ with open('model.pkl', 'rb') as file_5:
 
 def app():
     with st.form('from_fifa_2022'):
-        limit_balance = st.number_input('limit_balance', min_value=0, max_value=100, 
-                            value=50)
+        limit_balance = st.number_input('limit_balance', min_value=10000, max_value=780000, 
+                            value=10000)
         
         edu_choice = {1: "graduate school", 2: "university", 3: "high school", 4: "others"}
         def format_func_edu(option):
@@ -39,7 +39,7 @@ def app():
 
         sex = st.selectbox("Select option", options=list(sex_choice.keys()), format_func=format_func_sex)
         
-        age = st.number_input('Age', min_value=0, max_value=100, 
+        age = st.number_input('Age', min_value=0, max_value=69, 
                                     value=50)
         pay_0 = st.number_input('pay_0', min_value=0, max_value=3, 
                                     value=1)
@@ -47,42 +47,26 @@ def app():
                             value=1)
         pay_6 = st.number_input('pay_6', min_value=0, max_value=3, 
                          value=1)
-        bill_amt_1 = st.number_input('bill_amt_1', min_value=0, max_value=100, 
+        bill_amt_1 = st.number_input('bill_amt_1', min_value=-46627, max_value=259409, 
                                     value=50)
-        bill_amt_3 = st.number_input('bill_amt_3', min_value=0, max_value=100,
+        bill_amt_3 = st.number_input('bill_amt_3', min_value=-25443, max_value=242697,
                                     value=50)
-        bill_amt_5 = st.number_input('bill_amt_5', min_value=0, max_value=100,
+        bill_amt_5 = st.number_input('bill_amt_5', min_value=-11545, max_value=206378,
                                     value=50)
-        pay_amt_1 = st.number_input('pay_amt_1', min_value=0, max_value=100,
+        pay_amt_1 = st.number_input('pay_amt_1', min_value=0, max_value=15000,
                                     value=50)
-        pay_amt_2 = st.number_input('pay_amt_2', min_value=0, max_value=100,
+        pay_amt_2 = st.number_input('pay_amt_2', min_value=0, max_value=15000,
                                     value=50)
-        pay_amt_3 = st.number_input('pay_amt_3', min_value=0, max_value=100,
+        pay_amt_3 = st.number_input('pay_amt_3', min_value=0, max_value=15000,
                                     value=50)
-        pay_amt_4 = st.number_input('pay_amt_4', min_value=0, max_value=100,
+        pay_amt_4 = st.number_input('pay_amt_4', min_value=0, max_value=15000,
                                     value=50)
-        pay_amt_5 = st.number_input('pay_amt_5', min_value=0, max_value=100,
+        pay_amt_5 = st.number_input('pay_amt_5', min_value=0, max_value=15000,
                                     value=50)
-        pay_amt_6 = st.number_input('pay_amt_6', min_value=0, max_value=100,
+        pay_amt_6 = st.number_input('pay_amt_6', min_value=0, max_value=15000,
                                     value=50)
         #submit buttion
         submitted = st.form_submit_button('Predict')
-
-    #Inference
-    # limit_balance	
-    # education_level	
-    # age	
-    # pay_0	
-    # pay_3	
-    # pay_6	
-    # bill_amt_1	
-    # bill_amt_3	
-    # bill_amt_5	
-    # pay_amt_1	
-    # ...
-    # pay_amt_6	
-    # sex_1	
-    # martial_status_1	
     
     data_inf = {
         'limit_balance' : limit_balance,
@@ -136,7 +120,6 @@ def app():
         
         # transform to dataframe
         data_inf_num_scaled = pd.DataFrame(data_inf_num_scaled, columns=data_inf_num.columns)
-        # data_inf_cat_encoded = pd.DataFrame(data_inf_cat_encoded, columns=model_encoder.get_feature_names_out())
         capped_sex, ohe_source = encode_and_create_dataframe_train(data_inf_cat, 'sex')
         capped_martial_status, ohe_source = encode_and_create_dataframe_train(data_inf_cat, 'martial_status')
         
@@ -170,7 +153,10 @@ def app():
 
         y_pred_inf = model.predict(data_inf_final)
         
-        st.write('## Predicted Class: ', str(int(y_pred_inf)))
-
+        if y_pred_inf == 0:
+            st.write('## Predicted Class: ', 'Not Default')
+        else:
+            st.write('## Predicted Class: ', 'Default')
+        
 if __name__ == '__main__':
     app()