The Project uses the Fake News Classification on WELFake Dataset from Kaggle. Link - "https://www.kaggle.com/datasets/saurabhshahane/fake-news-classification"

📰 Fake News Detector

This project is a machine learning-based fake news detection system that classifies news content as real or fake. It uses natural language processing (NLP) techniques and a logistic regression model to identify misleading or fabricated news articles.

🔍 Features

• Combines news title and text to form the analysis content.
• Cleans and preprocesses text (removes noise, stopwords, punctuation).
• Applies stemming using the Porter Stemmer to normalize words.
• Converts text to numerical data using TF-IDF vectorization.
• Splits data into training and testing sets.
• Trains a Logistic Regression model.
• Evaluates performance using accuracy score.

📁 Dataset

The project uses the WELFake Dataset, which contains labeled news articles with their titles and full texts. The labels indicate whether the article is real (1) or fake (0).

🧠 Workflow Overview

1. Load Data Read the CSV file and handle missing values.
2. Combine Title and Text Merge the title and text columns to create a new content column.
3. Text Preprocessing
   • Convert text to lowercase
   • Remove non-alphabetical characters
   • Remove stopwords
   • Apply stemming using PorterStemmer
4. Feature Extraction Use TfidfVectorizer to convert preprocessed text into numerical feature vectors.
5. Model Training Split the dataset (80% train, 20% test) and train a Logistic Regression classifier.
6. Model Evaluation Evaluate accuracy on the test set to determine performance.

🛠️ Libraries & Tools

• pandas, numpy – Data handling
• nltk – Text preprocessing and stemming
• scikit-learn – Machine learning and evaluation