Python, Please. I have seen other posts with all wrong answers.

5. Implement ColumnTransformer for both numerical and categorical columns and assigned to a variable called preprocessor [ ] 6. Fit and transform the original data frame credit_df and assign the final output to transformed_data_df hints: - use the preprocessor object created in the previous to fit and transform on the credit_df. - use extract_feature_names method to get the feature names in order to create the final transformed_data_df [ ] y_test. [ ] [] 8. Build a k-nearest neighbors (KNN) classifier and train the model using the fit method. [] 9. Compute and display the percent accuracy score [] 10. Create a dataframe to hold both the Actual and Predicted labels and display the first 20 rows