Update app.py

app.py

@@ -6,10 +6,10 @@ from joblib import load
 model = load('loandefaulter.joblib')
 scaler = load('scaler.joblib')
 
-# Define numerical features for scaling
+# Define numerical features for scaling (only those that were used during training)
 num_features = [
     'loan_amnt', 'int_rate', 'installment', 'annual_inc', 'dti',
-    'revol_bal', 'revol_util', 'total_acc', 'mort_acc', 'cibil_score'
+    'revol_bal', 'revol_util', 'total_acc', 'mort_acc'
 ]
 
 # Create the Streamlit app
@@ -53,15 +53,15 @@ input_data = pd.DataFrame({
     'revol_bal': [revol_bal],
     'revol_util': [revol_util],
     'total_acc': [total_acc],
-    'mort_acc': [mort_acc],
-    'cibil_score': [cibil_score]
+    'mort_acc': [mort_acc]
 })
 
 # 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
+# Add the additional feature (not part of scaling)
 input_data['loan_amnt_by_income'] = [loan_amnt_by_income]
+input_data['cibil_score'] = cibil_score  # Add cibil_score directly without scaling
 
 # Predict using the model
 if st.button('Predict'):
@@ -71,5 +71,3 @@ if st.button('Predict'):
     st.markdown(f"""
         <div style="font-size: 24px; color: {color}; font-weight: bold;">Prediction: {result}</div>
     """, unsafe_allow_html=True)
-
-