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app.py

@@ -38,10 +38,17 @@ def predict_churn():
     input_data = pd.DataFrame([requestData])
 
     # create encoder with OneHotEncoder for encoding the selected values to match the training data
-    encoder = OneHotEncoder(handle_unknown='ignore', sparse_output=False) # Important for handling unseen categories
+    # encoder = OneHotEncoder(handle_unknown='ignore', sparse_output=False) # Important for handling unseen categories
 
     # You MUST use the *trained* encoder to transform the new data
-    encoded_new_data = encoder.transform(input_data[['Product_Sugar_Content','Product_Type','Store_Id','Store_Size','Store_Location_City_Type','Store_Type']])
+    # encoded_new_data = encoder.transform(input_data[['Product_Sugar_Content','Product_Type','Store_Id','Store_Size','Store_Location_City_Type','Store_Type']])
+
+    encoded_new_data = pd.get_dummies(
+      input_data,
+      columns=['Product_Sugar_Content','Product_Type','Store_Id','Store_Size','Store_Location_City_Type','Store_Type'],
+      drop_first=True,
+    );
+
 
     # Make a churn prediction using the trained model
     prediction = model.predict(encoded_new_data).tolist()[0]