https://github.com/punitkumar4871/fake_news_prediction
A simple Jupyter Notebook project 📓 to classify news articles as 🧐 Fake or ✅ Real using machine learning.
https://github.com/punitkumar4871/fake_news_prediction
matplotlib pandas python scikit-learn
Last synced: 4 months ago
JSON representation
A simple Jupyter Notebook project 📓 to classify news articles as 🧐 Fake or ✅ Real using machine learning.
- Host: GitHub
- URL: https://github.com/punitkumar4871/fake_news_prediction
- Owner: punitkumar4871
- Created: 2024-12-30T14:59:51.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-24T17:42:25.000Z (over 1 year ago)
- Last Synced: 2025-10-05T00:37:52.493Z (9 months ago)
- Topics: matplotlib, pandas, python, scikit-learn
- Homepage:
- Size: 80.1 KB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Fake News Prediction
A Jupyter Notebook project to classify news articles as **fake** or **real** based on a given dataset. The notebook includes data preprocessing, model training, and evaluation steps.
---
## Requirements
To run this project, you need the following:
- Python 3.7+
- Jupyter Notebook
- Required Python libraries (install using the command below):
```bash
pip install pandas numpy scikit-learn matplotlib seaborn nltk
```
---
## Dataset
The dataset used in this project contains:
- **Text data**: The content of the news articles.
- **Labels**: Whether the article is `Fake` or `Real`.
Ensure the dataset (e.g., `fake_news.csv`) is placed in the same directory as the notebook.
---
## Usage
1. Clone this repository or download the notebook:
```bash
git clone https://github.com/your_username/fake_news_prediction.git
cd fake_news_prediction
```
2. Launch the Jupyter Notebook:
```bash
jupyter notebook fake_news_prediction.ipynb
```
3. Follow the steps in the notebook to:
- Load and preprocess the dataset.
- Train a classification model.
- Evaluate its performance.
---
## Output
The notebook provides:
- Preprocessing insights.
- Accuracy, precision, recall, and F1-score of the model.
- Visualizations of the model’s performance.
---
## Technologies Used
- **Python**: Programming language.
- **Jupyter Notebook**: Code execution and visualization.
- **Libraries**:
- Pandas, NumPy: Data manipulation.
- Scikit-learn: Machine learning and evaluation.
- NLTK: Natural Language Processing.
- Matplotlib, Seaborn: Data visualization.
---
## License
This project is for educational purposes. Feel free to modify and use it as needed.