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FakeNewsPrediction

This repository contains a Python-based machine learning model that leverages logistic regression to accurately classify news articles as real or fake. The project utilizes a Kaggle dataset containing labeled news articles and employs natural language processing (NLP) techniques to extract meaningful features from the text.

Key Features:

  • Data Preprocessing: Cleans and prepares the dataset by removing stop words, stemming, and converting text to a numerical representation.
  • Feature Extraction: Extracts relevant features from the text data, such as TF-IDF scores and word embeddings.
  • Logistic Regression Model: Trains a logistic regression model on the extracted features to classify news articles as real or fake.
  • Evaluation: Evaluates the model's performance using metrics like accuracy, precision, recall, and F1-score.

How to Use:

  1. Clone the repository: git clone https://github.com/bhaveshGhanchi/FakeNewsPrediction.git
  2. Prepare data: Download the Kaggle dataset and place it in the data directory.
  3. Run the model: Execute the FakeNewsPrediction.ipynb script to train and evaluate the model.

Dependencies:

  • Python
  • Numpy
  • Pandas
  • Scikit-learn
  • NLTK

Contributions:

Contributions to this project are welcome! Feel free to fork the repository and submit pull requests with your improvements or new features.  

License:

This project is licensed under the MIT License.