Update app.py

app.py

@@ -7,7 +7,10 @@ model = load('loandefaulter.joblib')
 scaler = load('scaler.joblib')
 
 # Define numerical features for scaling
-num_features = ['loan_amnt', 'int_rate', 'installment', 'annual_inc', 'cibil_score']
+num_features = [
+    'loan_amnt', 'int_rate', 'installment', 'annual_inc', 'dti',
+    'revol_bal', 'revol_util', 'total_acc', 'mort_acc', 'cibil_score'
+]
 
 # Create the Streamlit app
 st.set_page_config(page_title='Loan Default Prediction', layout='wide')
@@ -30,6 +33,14 @@ int_rate = st.slider('Interest Rate (%)', min_value=0.0, max_value=30.0, step=0.
 installment = st.slider('EMI Amount', min_value=0.0, max_value=10000.0, step=10.0, value=200.0)
 annual_inc = st.slider('Annual Income', min_value=0.0, max_value=1000000.0, step=1000.0, value=50000.0)
 cibil_score = st.number_input('CIBIL Score', min_value=300, max_value=900, step=1, value=700)
+
+# Set default values for the missing features
+dti = 0.0  # Example default value for DTI
+revol_bal = 0.0  # Example default value for Revolving Balance
+revol_util = 0.0  # Example default value for Revolving Utilization
+total_acc = 0  # Example default value for Total Accounts
+mort_acc = 0  # Example default value for Mortgage Accounts
+
 loan_amnt_by_income = loan_amnt / (annual_inc + 1)
 
 # Create a DataFrame for the input
@@ -38,6 +49,11 @@ input_data = pd.DataFrame({
     'int_rate': [int_rate],
     'installment': [installment],
     'annual_inc': [annual_inc],
+    'dti': [dti],
+    'revol_bal': [revol_bal],
+    'revol_util': [revol_util],
+    'total_acc': [total_acc],
+    'mort_acc': [mort_acc],
     'cibil_score': [cibil_score]
 })
 
@@ -56,3 +72,4 @@ if st.button('Predict'):
         <div style="font-size: 24px; color: {color}; font-weight: bold;">Prediction: {result}</div>
     """, unsafe_allow_html=True)
 
+