4

  

import pandas as pd 

 

def find_s_algorithm(file_path): 

    # Load the dataset 

    data = pd.read_csv(file_path) 

    print("\nTraining Data:\n", data) 

 

    attributes = data.columns[:-1]  # All columns except the target 

    target = data.columns[-1]       # Target column (last one) 

 

    # Step 1: Initialize hypothesis with the first positive example 

    hypothesis = None 

    for _, row in data.iterrows(): 

        if row[target] == 'Yes': 

            hypothesis = list(row[attributes]) 

            break 

 

    # If no positive examples found 

    if hypothesis is None: 

        print("No positive examples in the dataset.") 

        return None 

 

    # Step 2: Generalize the hypothesis using other positive examples 

    for _, row in data.iterrows(): 

        if row[target] == 'Yes': 

            for i in range(len(attributes)): 

                if hypothesis[i] != row[attributes[i]]: 

                    hypothesis[i] = '?' 

 

    return hypothesis 

 

# Example usage 

file_path = r"your_dataset.csv"  # Replace with actual path 

hypothesis = find_s_algorithm(file_path) 

if hypothesis: 

    print("\nFinal Hypothesis:", hypothesis)

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