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app.py

@@ -33,19 +33,19 @@ def predict_sales():
 
     # Extract relevant lead features from the input data
     sample = {
-      'Age': lead_data['age'],
-      'Current_Occupation': lead_data['current_occupation'],
-      'First_Interaction': lead_data['first_interaction'],
-      'Profile_Completed': lead_data['profile_completed'],
-      'Website_Visits': lead_data['website_visits'],
-      'Time_Spent_on_Website': lead_data['time_spent_on_website'],
-      'Page_Views_Per_Visit': lead_data['page_views_per_visit'],
-      'Last_Activity': lead_data['last_activity'],
-      'Print_Media_Type1': lead_data['print_media_type1'],
-      'Print_Media_Type2': lead_data['print_media_type2'],
-      'Digital_Media': lead_data['digital_media'],
-      'Educational_Channels': lead_data['educational_channels'],
-      'Referral': lead_data['referral']
+      'age': lead_data['age'],
+      'current_occupation': lead_data['current_occupation'],
+      'first_interaction': lead_data['first_interaction'],
+      'profile_completed': lead_data['profile_completed'],
+      'website_visits': lead_data['website_visits'],
+      'time_spent_on_website': lead_data['time_spent_on_website'],
+      'page_views_per_Visit': lead_data['page_views_per_visit'],
+      'last_activity': lead_data['last_activity'],
+      'print_media_type1': lead_data['print_media_type1'],
+      'print_media_type2': lead_data['print_media_type2'],
+      'digital_media': lead_data['digital_media'],
+      'educational_channels': lead_data['educational_channels'],
+      'referral': lead_data['referral']
     }
 
     # Convert the extracted data into a Pandas DataFrame