Find out if any column have null values.
Used pivot_table to get Item_Identifier wise Item_Weight Calculated mean for each Item_Identifier and fill it in null value to respective Item_Weight
Identify 2410 null values of Outlet_Size Find out which Outlet_Type has missing values Filled mode of each Outlet_Size of respective Outlet_Type
Identify 526 "0" values in Item_Visibility and filled with mean value
Corrected typing error in Item_Fat_Content eg. Converted LF, low fat to Low Fat and reg to Regular
Added new column as New_Item_Type by getting first 2 charecter of Item_Identifier like FD, NC and DR and give them name as Food, Non-Consumable and Drinks respectively using grouby(), we find out Non-Consumable type is mapped to Low Fat category in Item_Content_Type, so marked it as Non-Edible in Item_Fat_Content column
Calculated total years of establishment
Count_of_Item_fat_content
Count_of_Item_Type
Count_of_Outlet_Establishment_Year
Count_of_outlet_location_type
Count_of_outlet_size
Count_of_outlet_type
Distribution_of_Item_Weight
