Importing necessary libraries

import pandas as pd import numpy as np

Loading the dataset

df = pd.read_csv('dataset.csv')

Dropping unnecessary columns

df = df.drop(['id', 'name', 'date'], axis=1)

Encoding the target variable

le = LabelEncoder() df['class'] = le.fit_transform(df['class'])

Converting the dataset into a dictionary

data = df.to_dict('records')

Vectorizing the features

vec = DictVectorizer() X = vec.fit_transform(data).toarray()

Splitting the data into training and testing sets

from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, df['class'], test_size=0.2, random_state=42)

Training the decision tree classifier

clf = DecisionTreeClassifier() clf.fit(X_train, y_train)

Predicting the target variable for the test data

y_pred = clf.predict(X_test)

Evaluating the performance of the model

print(classification_report(y_test, y_pred, target_names=le.classes_)