Upload app.py

app.py

@@ -29,24 +29,23 @@ avg_frequency_login_days = st.number_input('Input Average Frequency Login Days :
 points_in_wallet = st.number_input('Input Points in Wallet : ',0.0,2069.069760814851)
 used_special_discount = st.radio('Used Special Discount? ',('Yes','No'))
 offer_application_preference = st.radio('Offer Application Preference : ',('Yes','No'))
-feedback = st.selectbox('Select Feedback : ','Too many ads', 'No reason specified', 'Reasonable Price',
+feedback = st.selectbox('Select Feedback : ',('Too many ads', 'No reason specified', 'Reasonable Price',
                           'Quality Customer Care', 'Poor Website', 'Poor Customer Service',
-                          'Poor Product Quality', 'User Friendly Website', 'Products always in Stock')
+                          'Poor Product Quality', 'User Friendly Website', 'Products always in Stock'))
 
 if st.button('Predict'):
     data_inf = pd.DataFrame({'region_category' : region_category,
-                        'membership_category' : membership_category,
-                        'joined_through_referral' : joined_through_referral,
-                        'preferred_offer_types' : preferred_offer_types,
-                        'medium_of_operation' : medium_of_operation,
-                        'days_since_last_login' : days_since_last_login,
-                        'avg_time_spent' : avg_time_spent,
-                        'avg_transaction_value' : avg_transaction_value,
-                        'avg_frequency_login_days' : avg_frequency_login_days,
-                        'points_in_wallet' : points_in_wallet,
-                        'used_special_discount' : used_special_discount,
-                        'offer_application_preference' : offer_application_preference,
-                        'feedback' : feedback                       
-                        })
-    hasil = 'Not Churn' if func_imp.predict(data_inf) == 0 else 'Churn'
+                             'membership_category' : membership_category,
+                             'joined_through_referral' : joined_through_referral,
+                             'preferred_offer_types' : preferred_offer_types,
+                             'medium_of_operation' : medium_of_operation,
+                             'days_since_last_login' : days_since_last_login,
+                             'avg_time_spent' : avg_time_spent,
+                             'avg_transaction_value' : avg_transaction_value,
+                             'avg_frequency_login_days' : avg_frequency_login_days,
+                             'points_in_wallet' : points_in_wallet,
+                             'used_special_discount' : used_special_discount,
+                             'offer_application_preference' : offer_application_preference,
+                             'feedback' : feedback}, index=[0])
+    hasil = 'Not Churn' if np.round(func_imp.predict(pipeline.transform(data_inf))) == 0 else 'Churn'
     st.header(f'Prediksi = {hasil}')