2. Conduct Data Cleansing: Implement the following tasks: . . Drop the Class column from the created Business Class passenger satisfaction survey ("df_Business"). Rename "df_Business" columns by making all the columns' names in lowercase, and if the column name has whitespace, replace it with an underscore. (For example: "Customer Type" will be renamed to "customer_type"). Modify improperly formatted data and handle bad data. Handle the missing values (You should have zero missing values in your cleaned dataset). Remove the fully duplicated observations. Fix the columns datatypes. 3. Export the cleaned dataset to CSV file. . Use the following naming convention: Project_2_Group#.csv 4. Compare the Data Characteristics Before and After Data Cleansing. . At the end of the Jupyter Notebook, provide a brief write-up annotation to compare the characteristics of each column before and after data cleansing (this includes, the column name, datatype, unique values, and the number of missing values).