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.
PROGRAM: download csv file click here
import pandas as pd
def find_s_algorithm(file_path):
data = pd.read_csv(file_path)
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)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', '?']
The output is incorrect.
It should be [‘?’, ‘?’, ‘high’, ‘?’].
Try doing it manually—some changes need to be made:
1.Initialize hypothesis = None.
2.Use a for loop.
3.Add an if condition:
If hypothesis == None, set hypothesis = list(row[attributes]).
Please correct your code, as it has issues.