Update app.py

app.py

@@ -204,14 +204,15 @@ if submitted:
    
         
     # Getting date Features
+     # Getting date Features
     processed_data= getDateFeatures(df, 'date')
     processed_data= processed_data.drop(columns=['date'])
     
     # Encoding Categorical Variables
-    # encoder = preprocessing.LabelEncoder()
+    encoder = preprocessing.LabelEncoder()
     cols = ['family', 'city', 'state', 'store_type', 'locale', 'locale_name', 'day_type']
     for col in cols:
-        processed_data[cols] = encoder.fit_transform(processed_data[cols])
+        processed_data[col] = encoder.fit_transform(processed_data[col])
     
     # Making Predictions
     def predict(X, model= Loaded_object['model']):