ass4(decision Tree)

 import pandas as pd 

from sklearn.model_selection import train_test_split 

from sklearn.preprocessing import LabelEncoder 

from sklearn.tree import DecisionTreeClassifier 

from sklearn.metrics import accuracy_score 

# Load the dataset 

file_path = "drug.csv"  # Change this to your file path 

df = pd.read_csv(file_path) 

# Encode categorical variables 

label_encoders = {} 

for col in ["Sex", "BP", "Cholesterol", "Drug"]: 

le = LabelEncoder() 

df[col] = le.fit_transform(df[col]) 

label_encoders[col] = le 

# Define features and target variable 

X = df.drop(columns=["Drug"]) 

y = df["Drug"] 

# Split dataset into training and testing sets (80% train, 20% test) 

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, 

random_state=4) 

# Train a Decision Tree classifier with specified parameters 

dtree = DecisionTreeClassifier(criterion='entropy', max_depth=None) 

dtree_y_pred = dtree.fit(X_train, y_train).predict(X_test) 

# Calculate accuracy 

dtree_acc = accuracy_score(y_test, dtree_y_pred) 

print(f'Accuracy of Decision Tree Classifier: {dtree_acc * 100:.2f}%')