import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.svm import LinearSVC
from sklearn.metrics import classification_report

# Load the BBC News dataset
data = pd.read_csv('bbc-news.csv')

# Split the dataset into features (Article) and labels (Category)
X = data['Article']
y = data['Category']

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize the TF-IDF vectorizer
vectorizer = TfidfVectorizer()

# Transform the training set text into text-numeric vectors
X_train_vectors = vectorizer.fit_transform(X_train)

# Transform the testing set text into text-numeric vectors
X_test_vectors = vectorizer.transform(X_test)

# Initialize and train a linear support vector classifier (SVC)
classifier = LinearSVC()
classifier.fit(X_train_vectors, y_train)

# Make predictions on the testing set
y_pred = classifier.predict(X_test_vectors)

# Evaluate the model performance
print(classification_report(y_test, y_pred))