Machine Learning lab

Program

4. For a given set of training data examples stored in a .CSV file, implement and demonstrate the Find-S algorithm to output a description of the set of all hypotheses consistent with the training examples. 




import pandas as pd

def find_s_algorithm(file_path):
    data = pd.read_csv("training_data.csv")

    print("Training data:")
    print(data)

    attributes = data.columns[:-1]
    class_label = data.columns[-1]

    hypothesis = ['?' for _ in attributes]

    for index, row in data.iterrows():
        if row[class_label] == 'Yes':
            for i, value in enumerate(row[attributes]):
                if hypothesis[i] == '?' or hypothesis[i] == value:
                    hypothesis[i] = value
                else:
                    hypothesis[i] = '?'

    return hypothesis


file_path = 'training_data.csv'
hypothesis = find_s_algorithm(file_path)
print("\nThe final hypothesis is:", hypothesis)


Open Notepad & Create :- "training_data.csv" 


 
Outlook,Temperature,Humidity,Windy,PlayTennis
Sunny,Hot,High,FALSE,No
Sunny,Hot,High,TRUE,No
Overcast,Hot,High,FALSE,Yes
Rain,Cold,High,FALSE,Yes
Rain,Cold,High,TRUE,No
Overcast,Hot,High,TRUE,Yes
Sunny,Hot,High,FALSE,No


Output: 
Training data:

    Outlook Temperature Humidity  Windy  PlayTennis
0     Sunny      Hot     High    False         No
1     Sunny      Hot     High    True          No
2  Overcast      Hot     High    False         Yes
3      Rain      Cold    High    False         Yes
4      Rain      Cold    High    True          No
5  Overcast      Hot     High    True          Yes
6     Sunny      Hot     High    False         No
    
    The final hypothesis is: ['Overcast', 'Hot', 'High', '?']