Update app.py

app.py

@@ -5,52 +5,56 @@ import joblib
 # Load the bank_df_cleaned for reference
 bank_df_cleaned = pd.read_csv('bank_df_cleaned.csv')
 
-# Title and description
-st.title("Banking Campaign Outcome Prediction App")
-st.markdown("""
-Welcome to the **Banking Campaign Outcome Prediction App**!  
-This tool predicts whether a customer will respond positively to a banking campaign based on their demographic and transactional information.  
-Please input the required details below to get a prediction.
-""")
+# Check if 'job' column exists
+if 'job' not in bank_df_cleaned.columns:
+    st.error("The column 'job' does not exist in the DataFrame.")
+else:
+    # Title and description
+    st.title("Banking Campaign Outcome Prediction App")
+    st.markdown("""
+    Welcome to the **Banking Campaign Outcome Prediction App**!  
+    This tool predicts whether a customer will respond positively to a banking campaign based on their demographic and transactional information.  
+    Please input the required details below to get a prediction.
+    """)
 
-# Load the saved model
-model = joblib.load("HistGradientBoostingClassifier.joblib")
+    # Load the saved model
+    model = joblib.load("HistGradientBoostingClassifier.joblib")
 
-# Input features
-st.header("Enter the details for prediction:")
+    # Input features
+    st.header("Enter the details for prediction:")
 
-job = st.selectbox('Job', bank_df_cleaned['job'].unique())
-marital = st.selectbox('Marital', bank_df_cleaned['marital'].unique())
-education = st.selectbox('Education', bank_df_cleaned['education'].unique())
-default = st.selectbox('Default', bank_df_cleaned['default'].unique())
-housing = st.selectbox('Housing Loan', bank_df_cleaned['housing'].unique())
-loan = st.selectbox('Personal Loan', bank_df_cleaned['loan'].unique())
-poutcome = st.selectbox('Previous Outcome', bank_df_cleaned['poutcome'].unique())
-log_campaign = st.slider('Log Campaign', float(bank_df_cleaned['log_campaign'].min()), float(bank_df_cleaned['log_campaign'].max()))
-log_duration = st.slider('Log Duration', float(bank_df_cleaned['log_duration'].min()), float(bank_df_cleaned['log_duration'].max()))
-log_age = st.slider('Log Age', float(bank_df_cleaned['log_age'].min()), float(bank_df_cleaned['log_age'].max()))
-campaign_duration_interaction = log_campaign * log_duration
-contact_is_cellular = st.selectbox('Contact is Cellular', [0, 1])
-total_loans = st.selectbox('Total Loans', [0, 1, 2])
+    job = st.selectbox('Job', bank_df_cleaned['job'].unique())
+    marital = st.selectbox('Marital', bank_df_cleaned['marital'].unique())
+    education = st.selectbox('Education', bank_df_cleaned['education'].unique())
+    default = st.selectbox('Default', bank_df_cleaned['default'].unique())
+    housing = st.selectbox('Housing Loan', bank_df_cleaned['housing'].unique())
+    loan = st.selectbox('Personal Loan', bank_df_cleaned['loan'].unique())
+    poutcome = st.selectbox('Previous Outcome', bank_df_cleaned['poutcome'].unique())
+    log_campaign = st.slider('Log Campaign', float(bank_df_cleaned['log_campaign'].min()), float(bank_df_cleaned['log_campaign'].max()))
+    log_duration = st.slider('Log Duration', float(bank_df_cleaned['log_duration'].min()), float(bank_df_cleaned['log_duration'].max()))
+    log_age = st.slider('Log Age', float(bank_df_cleaned['log_age'].min()), float(bank_df_cleaned['log_age'].max()))
+    campaign_duration_interaction = log_campaign * log_duration
+    contact_is_cellular = st.selectbox('Contact is Cellular', [0, 1])
+    total_loans = st.selectbox('Total Loans', [0, 1, 2])
 
-data = {
-    'job': job,
-    'marital': marital,
-    'education': education,
-    'default': default,
-    'housing': housing,
-    'loan': loan,
-    'poutcome': poutcome,
-    'log_campaign': log_campaign,
-    'log_duration': log_duration,
-    'log_age': log_age,
-    'campaign_duration_interaction': campaign_duration_interaction,
-    'contact_is_cellular': contact_is_cellular,
-    'total_loans': total_loans
-}
-input_df = pd.DataFrame(data)
+    data = {
+        'job': job,
+        'marital': marital,
+        'education': education,
+        'default': default,
+        'housing': housing,
+        'loan': loan,
+        'poutcome': poutcome,
+        'log_campaign': log_campaign,
+        'log_duration': log_duration,
+        'log_age': log_age,
+        'campaign_duration_interaction': campaign_duration_interaction,
+        'contact_is_cellular': contact_is_cellular,
+        'total_loans': total_loans
+    }
+    input_df = pd.DataFrame(data)
 
-# Make prediction
-prediction = model.predict(input_df)
-outcome = "will subscribe to" if prediction[0] == 1 else "will not subscribe to"
-st.success(f"Prediction: The customer {outcome} term deposit.")
+    # Make prediction
+    prediction = model.predict(input_df)
+    outcome = "will subscribe to" if prediction[0] == 1 else "will not subscribe to"
+    st.success(f"Prediction: The customer {outcome} term deposit.")