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

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

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

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

def display(self):
    if not self.is_empty():
        print("Stack:", self.items[::-1])
    else:
        print("Stack is empty")

Menu-driven program

stack = Stack()

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

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

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

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

elif choice == 3:
    stack.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, Binarizer

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. Binarizing Data: Using the Binarizer class

binarizer = Binarizer(threshold=0.5)
binarized_data = binarizer.fit_transform(wine_data)

Display binarized data

print("\nBinarized Data:")
print(pd.DataFrame(binarized_data, columns=wine_data.columns).head())

Hslip23