Q1]
class Queue:
def init(self):
self.items = []

def is_empty(self):
    return len(self.items) == 0

def enqueue(self, item):
    self.items.append(item)

def dequeue(self):
    if not self.is_empty():
        return self.items.pop(0)
    else:
        return None

def display(self):
    if not self.is_empty():
        print("Queue:", self.items)
    else:
        print("Queue is empty")

Menu-driven program

queue = Queue()

while True:
print("\nQueue Operations:")
print("1. Insert an element in queue")
print("2. Delete an element from queue")
print("3. Display the contents of queue")
print("4. Exit")

choice = int(input("Enter your choice (1-4): "))

if choice == 1:
    element = int(input("Enter the element to insert: "))
    queue.enqueue(element)
    print(f"{element} inserted into the queue.")

elif choice == 2:
    deleted_element = queue.dequeue()
    if deleted_element is not None:
        print(f"{deleted_element} deleted from the queue.")
    else:
        print("Queue is empty. Cannot delete.")

elif choice == 3:
    queue.display()

elif choice == 4:
    print("Exiting the program.")
    break

else:
    print("Invalid choice. Please enter a valid option (1-4).")

Q2]
import pandas as pd
from sklearn.preprocessing import MinMaxScaler, StandardScaler, Normalizer

Load the dataset

dataset_path = "winequality-red.csv"
wine_data = pd.read_csv(dataset_path)

a. Rescaling: Normalizing the dataset using MinMaxScaler class

min_max_scaler = MinMaxScaler()
rescaled_data = min_max_scaler.fit_transform(wine_data)

Display rescaled data

print("\nRescaled Data:")
print(pd.DataFrame(rescaled_data, columns=wine_data.columns).head())

b. Standardizing Data: Transforming data into a standard Gaussian distribution

standard_scaler = StandardScaler()
standardized_data = standard_scaler.fit_transform(wine_data)

Display standardized data

print("\nStandardized Data:")
print(pd.DataFrame(standardized_data, columns=wine_data.columns).head())

c. Normalizing Data: Rescaling each observation to a length of 1 (a unit norm)

normalizer = Normalizer()
normalized_data = normalizer.fit_transform(wine_data)

Display normalized data

print("\nNormalized Data:")
print(pd.DataFrame(normalized_data, columns=wine_data.columns).head())

Hslip22