Consider the data given in CSV file HW3_Data_B. Do the following tasks using the above data:. Answer the following questions: Note: Solve all the following questions using Python (not by hand). Use Seaborn libraries for all the following plots. "differentiated by" in the following problem means that either have multiple plots, one plot with different colors, or one plot with sub-plots. B-1: Draw the histograms of all numeric and non-numeric columns. B-2: Using the histograms generated in B-1, provide descriptive comments on the distribution of the following columns: loan amount rate_of_interest loan_type property_value Hint: the descriptive comments may include about the similarity of the distribution with normal distribution or uniform distribution, about skewness, about mode, etc. B-3: Draw a plot between loan amount and Gender. What can you conclude from the plot? B-4: Draw a plot between property value and loan amount, differentiated by Status. For the de- faulted loans, what is the relationship between property-value and loan amount. (Note: A status of 1 indicates that the loan has defaulted.) B-5: Draw a plot between property value and loan amount, differentiated by Status and Region. Make the figsize=(9,4), and dpi=200. Hint: dpi is a keyword argument in plt.figure() method. B-6: Display a count plot of credit_type, differentiated by loan_purpose. B-7: Draw a plot on the loan amount differentiated by business_or_commercial and occupancy_type B-8: Add two new columns to the dataframe with headers: property value multiple and loan mul- tiple. The values in the new columns can be calculated using the following formulas: property value multiple = property_value income x 12 loan multiple = loan amount income x 12 B-9: Plot the Credit Score in ascending order on the x-axis, and the above two new columns on y-axis (Hint: use line plots)