train.py

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
import joblib

# Load datasets
fake = pd.read_csv("Fake.csv")
real = pd.read_csv("True.csv")

fake['label'] = 0
real['label'] = 1

df = pd.concat([fake, real], axis=0)
df = df.sample(frac=1).reset_index(drop=True)

X = df['text']
y = df['label']

# Split data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Vectorization
vectorizer = TfidfVectorizer(stop_words='english', max_df=0.7)
X_train_vec = vectorizer.fit_transform(X_train)
X_test_vec = vectorizer.transform(X_test)

# Train model
model = LogisticRegression(max_iter=1000)
model.fit(X_train_vec, y_train)

# Evaluate
preds = model.predict(X_test_vec)
acc = accuracy_score(y_test, preds)
print("Model Accuracy:", acc)

# Save model
joblib.dump(model, "fake_news_model.pkl")
joblib.dump(vectorizer, "vectorizer.pkl")

print("Model & vectorizer saved successfully!")