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Fake News Detection with Machine Learning

This repository contains a complete pipeline for building and deploying a Fake News Detection model. The project uses Python libraries like Pandas, Scikit-Learn, and NLTK to preprocess data, train models, and evaluate performance.

📂 Project Structure

  • Data Loading and Cleaning: Loads data, removes unnecessary columns, and preprocesses text.
  • Exploratory Data Analysis (EDA): Visualizations using Seaborn and WordCloud.
  • Feature Extraction: Uses TfidfVectorizer to convert text to numeric features.
  • Model Training: Trains models like Logistic Regression and Decision Tree for binary classification.
  • Evaluation: Compares model accuracy and displays the confusion matrix.

🔍 Features

  • Preprocessing: Removes stopwords, punctuations, and performs tokenization.
  • Visualization: Word clouds and bar charts for understanding word frequency.
  • Modeling: Train and test models (Logistic Regression, Decision Tree) for fake news detection.
  • Prediction Function: A simple function to predict if a news article is real or fake.

🚀 Getting Started

  1. Clone the Repository: 
    git clone https://github.com/Anju-Devi/Fake-News-Detection-Using-Machine-Learning.git