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https://github.com/docsallover/fake-news-detection
Fake News Detection Using NLP
https://github.com/docsallover/fake-news-detection
fakenewsdetection machine-learning machine-learning-algorithms machinelearning machinelearning-python matplotlib nlp nlp-machine-learning numpy pandas scikit-learn seaborn
Last synced: 3 months ago
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Fake News Detection Using NLP
- Host: GitHub
- URL: https://github.com/docsallover/fake-news-detection
- Owner: docsallover
- License: mit
- Created: 2024-09-17T15:35:49.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-09-17T17:14:16.000Z (4 months ago)
- Last Synced: 2024-10-16T20:19:34.236Z (3 months ago)
- Topics: fakenewsdetection, machine-learning, machine-learning-algorithms, machinelearning, machinelearning-python, matplotlib, nlp, nlp-machine-learning, numpy, pandas, scikit-learn, seaborn
- Language: Jupyter Notebook
- Homepage: https://docsallover.com/blog/data-science/fake-news-detection-using-nlp-to-identify-misinfor/
- Size: 41.2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Fake News Detection using Machine Learning
This project is a fake news detection system that uses machine learning algorithms to classify news articles as either fake or real. The system uses two datasets from Kaggle, namely "Fake" and "True", to train the model. The two datasets contain news articles labeled as either fake or real.
## Dataset
Dataset Link: https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset
## Classifiers
Five classifiers are used in this project they are Naive Bayes, Random Forest, Decision Tree, SVM, Logistic Regression.
## Overview
Fake news detection is a type of text classification problem. The system uses machine learning algorithms to classify news articles as either fake or real. The system consists of three main components:
1. Data preprocessing: This step involves cleaning and preprocessing the datasets to prepare them for analysis. The datasets contain information about news articles such as their text, title, and author.
2. Model training: This step involves training machine learning models to classify news articles as either fake or real. The models are trained using the preprocessed datasets.
3. Model evaluation: This step involves evaluating the performance of the trained models. The performance of the models is evaluated using metrics such as accuracy, precision, recall, and F1 score.
## How to Use
To use the system, follow these steps:
1. Clone the repository.
2. Create a virtual environment (venv or virtualenv) in the project directory.
3. Activate the virtual environment.
4. Install the required dependencies.
- Run `pip install -r requirements.txt`.
5. Run the `fake-news-detection.py` file to execute the system.
- If you are using Python 3, you can run `python fake-news-detection.py`.
6. Alternatively, you can run the `fake-news-detection.ipynb` notebook file in Jupyter Notebook/JupyterLab.
7. Enter your news article when prompted.
8. The system will classify the news article as either fake or real.
Note: The system is provided in both `.py` and `.ipynb` file formats.
## Dependencies
The system requires the following dependencies:
- pandas
- numpy
- matplotlib
- wordcloud
- seaborn
- nltk
- scikit-learn
## License
This project is licensed under the MIT License. See the LICENSE file for more details.
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