Use the given data $"$HW$2\_$Data$\_$B$"$ for the following tasks:

B$1.(4$ points$)$Read and display the dataset provided. Determine the number of rows and columns present. Additionally, identify the columns containing missing data, list their names $($if any$).$

B$2.(5$ points$)$ From the given dataset using the python script identify the columns with categorical data. Furthermore, identify every column type. Indicate the type of consistency in the given dataset, if any. Convert the $$Id$$ column from numerical to object type for ease of numeric operation such as normalization. $$

B$3.(6$ points$)$Look at the given dataset. Using python commands filter out negative values in the following two columns $$bmi$$ and $$children$.$ Furthermore, some values are in decimal by mistake in $$age$$ columns. Correct it using appropriate method. Also, find the unique categorical values and remove $$unknown$$ values, if any $($note that Nan is not considered as unknown$).$

B$4.(6$ points$)$Drop all columns containing $23\%$ or more missing values. Then impute the columns having missing values using median if the column is numerical and using mode if the column is categorical.

B$5.(6$ points$)$Transform the $$charges$$ column such that the minimum value is $0$ and maximum value is $1.$ Furthermore, transform the $$age$$ column to have a mean of zero and a standard deviation of one. $$Print only the transformed columns.

B$6.(5$ points$)$Discretize "bmi" column into the following four bins using only Pandas. Save it into another column as bmi$\_$status.

bmi

Bin

Below $18\mathrm{.}5$

Underweight

$18\mathrm{.}524\mathrm{.}9$

Healthy Weight

$25\mathrm{.}029\mathrm{.}9$

Overweight

$30\mathrm{.}0$ and Above

$$Obesity

B$7.(4$ points$)$Convert $$region$$ using one$-$hot encoder. The new name should start with $$reg$($reg$\_$northeast$).$ Remove the original column.

B$8.(4$ points$)$For the column $$smoker$,$ convert it yes to $1$ and $$no to $0.$ The column name should remain unchanged.