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

Problem: Use the credit.csv dataset to build classification model using KNN. The target variable is default which is a binary label to indicate of the Ioan is default (yes, no). Use all other variables for your feature set. 1. Read credit.csv into a dataframe credit_df. Display the first 5 rows and number of rows and columsn in dataframe hint: Handel the missing value character '?' as na value.* [ ] 2. Print the total number of rows that have missing values [ ] 3. Determine categorical and numerical features and assign each into numerical_features and categorical_features [ ] 4. Create the preprocessing pipelines for both numeric and categorical data that does imputation, scaling and ONeHotEncoder to the appropriate columns. Use mean strategy for numerical imputation and most frequent for categorical imputation Use MinMaxScaler for data scaling. Enable drop first method in the OneHotEncoder constructor