Fake News Detection
Overview
This project aims to detect fake news using machine learning algorithms. It utilizes a dataset consisting of real and fake news articles to train various classification models and then uses these models to predict whether a given news article is fake or not.

Table of Contents
Installation
Usage
Data
Models
Contributing
License
Installation <a name="installation"></a>
To run this project, you need to have Python installed on your system along with the necessary libraries. You can install the required libraries using pip:

Copy code
pip install pandas numpy seaborn matplotlib scikit-learn
Usage <a name="usage"></a>
Clone this repository to your local machine.
Extract the contents of the provided dataset ZIP files into the /content directory.
Run the provided Python script fake_news_detection.py.
python
Copy code
python fake_news_detection.py
Follow the instructions in the console to input a news article for classification.
The program will output the prediction from each trained model: Logistic Regression, Decision Tree, Gradient Boosting, and Random Forest.
Data <a name="data"></a>
The dataset used in this project consists of two CSV files:

Fake.csv: Contains fake news articles.
True.csv: Contains real news articles.
Models <a name="models"></a>
This project uses the following machine learning models for classification:

Logistic Regression
Decision Tree
Gradient Boosting
Random Forest
Contributing <a name="contributing"></a>
Contributions to this project are welcome. You can contribute by:

Reporting issues
Suggesting enhancements
Adding new features
Fixing bugs
Please fork this repository, make your changes, and submit a pull request.

License <a name="license"></a>
This project is licensed under the MIT License.

Feel free to customize the content according to your specific project details and requirements. Make sure to include accurate and up-to-date information to assist users in understanding and using your project effectively.