Update app.py

app.py

@@ -8,7 +8,7 @@ scaler = load('scaler.joblib')
 
 # Define numerical features for scaling
 num_features = [
-    'loan_amnt', 'int_rate', 'installment', 'annual_inc', 'dti', 'revol_bal', 'revol_util', 'total_acc', 'mort_acc', 'loan_amnt_by_income'
+    'loan_amnt', 'int_rate', 'installment', 'annual_inc', 'dti', 'revol_bal', 'revol_util', 'total_acc', 'mort_acc'
 ]
 
 # Create the Streamlit app
@@ -48,13 +48,15 @@ input_data = pd.DataFrame({
     'revol_bal': [revol_bal],
     'revol_util': [revol_util],
     'total_acc': [total_acc],
-    'mort_acc': [mort_acc],
-    'loan_amnt_by_income': [loan_amnt_by_income]
+    'mort_acc': [mort_acc]
 })
 
-# Scale the input data
+# Scale the numerical features that were used to fit the scaler
 input_data[num_features] = scaler.transform(input_data[num_features])
 
+# Add the new feature
+input_data['loan_amnt_by_income'] = [loan_amnt_by_income]
+
 # Predict using the model
 if st.button('Predict'):
     prediction = model.predict(input_data)