a2

import pandas as pd # Import pandas for data manipulation
# Load the dataset
data = pd.read_csv("P2Data.csv") # Load CSV file into a DataFrame
print(data) # Print the dataset to understand its structure
# Extract attributes and target
attributes = data.iloc[:, :-1].values # Extract all columns except
the last (features)
print("The attributes are:", attributes) # Print the attributes
target = data.iloc[:, -1].values # Extract the last column (target
labels)
print("The target is:", target) # Print the target
# Define the Find-S algorithm
def find_s(attributes, target):
 for i, value in enumerate(target): # Look for the first positive
example
 if value == "Yes":
 hypothesis = attributes[i].copy() # Initialize the
hypothesis
 break
 for i, value in enumerate(target): # Iterate through all examples
 if value == "Yes": # Consider only positive examples
 for j in range(len(hypothesis)): # Iterate through
attributes
 if attributes[i][j]!= hypothesis[j]: # If attributes
differ
 hypothesis[j] = '?' # Generalize to '?'

 return hypothesis # Return the final hypothesis
# Find and print the final hypothesis
final_hypothesis = find_s(attributes, target)
print("The final hypothesis is:", final_hypothesis)