Question 1: In this question, you will use the "Titanic" dataset. This dataset contains information on 891 passengers, with the following columns: Column Description sex Gender (“male” or “female”) age Age in years (float; some missing) sibsp Number of siblings/spouses aboard parch Number of parents/children aboard fare Ticket fare in British pounds embarked Port of embarkation (“C” = Cherbourg; “Q” = Queenstown; “S” = Southampton) class Passenger class (“First”, “Second”, or “Third”) alone Was the passenger travelling alone (True or False) survived Survival indicator (0 = No, 1 = Yes) Write a Python program to perform the following tasks/answer the following questions: 1. Load the dataset into a Pandas DataFrame and display the first five rows. 2. Plot a pie chart showing the percentage of passengers who embarked at each port. 3. Create a barplot showing the number of passengers travelling in each class. 4. Create a scatter plot of age vs. fare, colored by class. Based on this plot, state which passenger class generally paid the highest fares. 5. Calculate and print the mean and five-number summary of fare using the describe() command. 6. Create a box plot of fare and identify any outliers. 7. Plot a histogram of age and describe its distribution shape. 8. Filter the female passengers data and create a bar plot of their class counts. 9. Filter the male passengers data and create a bar plot of their class counts. Compare the two distributions. 10. Generate a heatmap of correlations among the numeric features of the data frame (age, fare, sibsp, parch); identify the strongest and weakest correlations. Question 2: In this question, you will use the "Penguins" dataset. The dataset contains measurements for penguin specimens from three islands in Antarctica. Each row represents one penguin. The data provided include: Column Description species Penguin species (Adelie, Chinstrap, Gentoo) Island Island where the penguin was observed (Torgersen, Biscoe, Dream) bill_length_mm Bill length in millimeters (float) bill_depth_mm Bill depth in millimeters (float) flipper_length_mm Flipper length in millimeters (float) body_mass_g Body mass in grams (float) sex Sex of the penguin (male, female) Write a Python program to perform the following tasks/answer the following questions: 1. Load the dataset from penguins.csv into a Pandas DataFrame and display the first five rows. 2. Use the command info() to print a description of all columns. How many penguins are included in this data frame? 3. Construct a pie chart showing the proportion of each penguin specie. 4. Build a bar plot of penguin counts by island. 5. Calculate and print the mean and five-number summary of bill_length_mm using describe(). 6. Construct a box plot of flipper_length_mm. From the box plot, identify if there are any outliers. 7. Plot a histogram of body_mass_g. Based on this plot, describe the distribution of the penguins body mass. 8. Filter the data frame to "Adelie" penguins and plot a histogram of their body_mass_g. 9. Filter the DataFrame to "Gentoo" penguins and plot a histogram of their body_mass_g. Compare with the histogram in part (8) and state which specie tend to be heavier. 10. Generate a heatmap of the correlation matrix among all numeric variables (bill_length_mm, bill_depth_mm, flipper_length_mm, body_mass_g); from the heatmap, identify the strongest correlation and the two weakest correlations.