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import pandas as pd from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import classification_report # 1. Cargar dataset df = pd.read_csv("emails_phishing.csv") X_train, X_test, y_train, y_test = train_test_split( df['text'], df['label'], test_size=0.2, random_state=42) # 2. Vectorización TF-IDF vectorizer = TfidfVectorizer(max_features=3000) X_train_vec = vectorizer.fit_transform(X_train) X_test_vec = vectorizer.transform(X_test) # 3. Entrenamiento model = RandomForestClassifier(n_estimators=200, random_state=42) model.fit(X_train_vec, y_train) # 4. Evaluación y_pred = model.predict(X_test_vec) print(classification_report(y_test, y_pred))
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