Delete model.pkl and vectorizer.pkl

model.pkl and vectorizer.pkl

@@ -1,52 +0,0 @@
-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 nltk.corpus import stopwords
-from nltk.stem import PorterStemmer
-import string
-import nltk
-import pickle
-
-nltk.download('stopwords')
-
-# Load data
-true_df = pd.read_csv("True.csv")
-fake_df = pd.read_csv("Fake.csv")
-
-true_df["label"] = 1
-fake_df["label"] = 0
-
-df = pd.concat([true_df, fake_df], axis=0).reset_index(drop=True)
-
-# Text Cleaning
-stemmer = PorterStemmer()
-stop_words = stopwords.words("english")
-
-def clean_text(text):
-    text = text.lower()
-    text = "".join([c for c in text if c not in string.punctuation])
-    tokens = text.split()
-    tokens = [stemmer.stem(word) for word in tokens if word not in stop_words]
-    return " ".join(tokens)
-
-df["cleaned_text"] = df["text"].apply(clean_text)
-
-# TF-IDF and Model Training
-X = df["cleaned_text"]
-y = df["label"]
-
-tfidf = TfidfVectorizer()
-X_tfidf = tfidf.fit_transform(X)
-
-X_train, X_test, y_train, y_test = train_test_split(X_tfidf, y, test_size=0.2, random_state=42)
-
-model = LogisticRegression()
-model.fit(X_train, y_train)
-
-# Save model and vectorizer
-with open("model.pkl", "wb") as f:
-    pickle.dump(model, f)
-
-with open("vectorizer.pkl", "wb") as f:
-    pickle.dump(tfidf, f)