Update app.py

app.py

@@ -446,13 +446,25 @@ elif option == "Financial Recommender Engine":
     else:
         st.write("Click the 'Get Recommendations' button to receive personalized financial recommendations.")
 
+
 elif option == "SML Classification":
     st.markdown("<h2 style='text-align: center;'>SML Classification</h2>", unsafe_allow_html=True)
 
+    # Age group labels with age ranges
+    age_group_labels = [
+        'Teen (13-18)',
+        'Young Adult (19-24)',
+        'Adult (25-34)',
+        'Middle Age (35-44)',
+        'Older Adult (45-54)',
+        'Senior (55-64)',
+        'Elder (65-110)'
+    ]
+
     # Collect user inputs
     place_of_living = st.selectbox('Place of Living', ['Urban Area', 'Rural Area', 'Unknown'])
     education_level = st.selectbox('Education Level', ['Primary', 'Secondary', 'Tertiary'])
-    age_group = st.selectbox('Age Group', ['Adult', 'Middle Age', 'Older Adult', 'Senior', 'Teen', 'Young Adult'])
+    age_group = st.selectbox('Age Group', age_group_labels)  # Dropdown with age ranges
     income_bracket = st.selectbox('Income Bracket', [1, 2, 3, 4, 5])
     female = st.radio('Gender', ['Female', 'Male'])
     is_mobileowner = st.checkbox('Is Mobile Owner?')
@@ -473,11 +485,12 @@ elif option == "SML Classification":
         'Has Internet Access': [1 if has_internet_access else 0],
         'Employed': [1 if employed else 0],
         'High Income Region': [1 if high_income_region else 0],
-        'Income Bracket': [income_bracket]  # Directly use Income Bracket as numerical
+        'Income Bracket': [income_bracket]
     })
 
+    # OneHotEncode only the categorical variables
     cat_encoded = pd.DataFrame(ohe.transform(cat_features), 
-                           columns=ohe.get_feature_names_out(['Place of living', 'Education Level', 'Age Group']))
+                               columns=ohe.get_feature_names_out(['Place of living', 'Education Level', 'Age Group']))
 
     # Combine categorical and numerical features
     features = pd.concat([num_features, cat_encoded], axis=1)
@@ -487,8 +500,11 @@ elif option == "SML Classification":
         # Make the prediction
         predicted_saved = xgb_model.predict(features)[0]
 
-        # Display prediction result
-        st.metric(label="Predicted Probability of Saving", value=f'{round(predicted_saved * 100, 2)}%')
+        # Display saving habit likelihood instead of probability
+        if predicted_saved >= 0.5:
+            st.success("Saving habit is likely.")
+        else:
+            st.error("Saving habit is unlikely.")
 
         # SHAP explanation
         st.subheader('Feature Contributions 🤖')