Update app.py

app.py

@@ -446,25 +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)'
-    ]
+     # Introduction to the SML Classification page
+    st.write("""
+    ### Predicting Savings Behavior
+    This page uses a **Supervised Machine Learning** model to predict whether a person has saved money in the past year.
+    Based on the information you provide, the model will analyze your input and predict:
+    
+    - **Saving habit is likely**: If the model predicts that you're likely to have saved money.
+    - **Saving habit is unlikely**: If the model predicts that you haven't likely saved money.
+
+    The prediction is based on variables such as your age, income, employment status, and other socioeconomic factors.
+    """)
 
     # 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', age_group_labels)  # Dropdown with age ranges
+    age_group = st.selectbox('Age Group', ['Adult', 'Middle Age', 'Older Adult', 'Senior', 'Teen', 'Young Adult'])
     income_bracket = st.selectbox('Income Bracket', [1, 2, 3, 4, 5])
     female = st.radio('Gender', ['Female', 'Male'])
     is_mobileowner = st.checkbox('Is Mobile Owner?')
@@ -485,12 +485,11 @@ 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]
+        'Income Bracket': [income_bracket]  # Directly use Income Bracket as numerical
     })
 
-    # 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)