1 Which of the following statements would retrieve the number of rows and columns in a data frame called df_1? df_1.head() df_1.info() df_1.rows_and_cols() df_1.shape 2 In binary classification how many values can the target variable have? As many as you want 3 2 1 3 What is the purpose of the following piece of code df_1.group('a')['b'].value_counts()? Return the unique value counts for each value in column b Group rows according to the unique values in column b, for each group return the unique value counts for each value in column a Return the unique value counts for each value in column a Group rows according to the unique values in column a, for each group return the unique value counts for each value in column b 4 Which of the following statements returns a statistical summary for the values in the column? df_1['c'].stats() df_1['c').info() df_1['c].describe() df_1['c').value_counts() 4 Which of the following statements returns a statistical summary for the values in the column? df_1['c'].stats() df_1['c').info() df_1['c].describe() df_1['c').value_counts() 5 When would binning be an appropriate feature engineering step? When we want to remove low-quality features When we want to create a new feature by combining existing ones When we want to transform continuous features into categorical features When we want to transform categorical features into continuous features