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Detecting Fake News with Python and Machine Learning: This repository features a news classification project with Python, utilizing TF-IDF vectorization and a Passive Aggressive Classifier. It includes interactive Jupyter Notebook widgets for exploring data splits and visualizing model performance.

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Detecting Fake News with Python and Machine Learning

Welcome to the Detecting Fake News with Python and Machine Learning repository! This project demonstrates how to classify news articles as 'FAKE' or 'REAL' using machine learning techniques. The interactive Jupyter Notebook allows you to experiment with different data splits and visualize model performance.

Features

  • Data Preprocessing: Load and clean news data.
  • Text Vectorization: Convert text data into numerical features using TF-IDF.
  • Machine Learning Model: Train a Passive Aggressive Classifier for news classification.
  • Interactive Exploration: Use Jupyter Notebook widgets to adjust parameters and visualize results.

Getting Started

Prerequisites

Ensure you have the following installed:

  • Python 3.x
  • Jupyter Notebook

Installation

  1. Clone the repository:

    git clone https://github.com/yourusername/Detecting-Fake-News-with-Python-and-Machine-Learning.git

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Detecting Fake News with Python and Machine Learning: This repository features a news classification project with Python, utilizing TF-IDF vectorization and a Passive Aggressive Classifier. It includes interactive Jupyter Notebook widgets for exploring data splits and visualizing model performance.

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