Create a function, preprocess$\_$data, which performs data preprocessing for a classification task. The function will preprocess the data by performing the following steps: $1.$ For categorical variables: $$ replace NaN values with the most frequent value; $$ create dummy variables based on levels $($all presented values$)$ and drop the first one in alphabetical order. Name the new binary columns using this schema: name of categorical variable $+-+$ level name. $2.$ For numerical variables: $$ replace NaN values with the median; $$ standardize values by subtracting the mean and dividing by the standard deviation. $3.$ For the target variable: $$ convert text values into integers, so that the first text value alphabetically is converted to $0$ and so on$.$ The preprocess$\_$data function accepts one argument: $$ dataframe $-$ pandas DataFrame where target is a classification label and other variables are explanatory variables. The function returns a tuple $($X$,$ y$),$ where: $$ X is a pandas DataFrame obtained after performing the preprocessing of numerical and categorical variables and after dropping the target column; $$ y is a list of values of the target variable after preprocessing. Example For this sort of data: $|$target married $|$degree Isalary loccupation$|$ the preprocess$\_$data function should return the following tuple: $->$ All changes saved $38\backslash$deg F Cloudy Q Search To lea Tes male $01111\mathrm{.}5$ nurse I female $1112$ NaN nurse I female $111312\mathrm{.}3$ policeman I male $111212\mathrm{.}0|$fireman male $101311\mathrm{.}5$ NaN