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.