https://github.com/afmontoya/fake-news-detection
Fake News Detection Using Machine Learning
https://github.com/afmontoya/fake-news-detection
fake-news-detection jupyter machine-learning nlp
Last synced: 2 months ago
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Fake News Detection Using Machine Learning
- Host: GitHub
- URL: https://github.com/afmontoya/fake-news-detection
- Owner: afmontoya
- License: mit
- Created: 2025-02-03T06:38:48.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-02-04T08:06:00.000Z (8 months ago)
- Last Synced: 2025-02-11T09:54:05.343Z (8 months ago)
- Topics: fake-news-detection, jupyter, machine-learning, nlp
- Language: Jupyter Notebook
- Homepage:
- Size: 12.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 📰 Fake News Detection Using Machine Learning 🔍🤖
## 📌 Project Overview
Fake news is a major issue in today's digital world, where misinformation spreads rapidly. This project aims to **detect fake news articles using machine learning techniques**. By analyzing text patterns and linguistic features, the model predicts whether a given news article is **real or fake**.
---
## 🚀 Features
- 📝 **Text Preprocessing** (Tokenization, Stopword Removal, Lemmatization)
- 🔢 **TF-IDF Vectorization** to convert text into numerical features
- 🤖 **Machine Learning Models** (Logistic Regression, Naive Bayes)
- 📊 **Exploratory Data Analysis (EDA)**
- 🌐 **API Integration (Planned)** (Serve the model via Flask/FastAPI)
- 🎨 **Web App (Planned)** (User-friendly frontend using Streamlit/React)
---
## 💻 Installation & Setup
Follow these steps to set up and run the project locally.
### **1️⃣ Clone the Repository**
```bash
git clone https://github.com/your-username/fake-news-detection.git
cd fake-news-detection
```
### **2️⃣ Create a Virtual Environment**
```bash
python3 -m venv venv
source venv/bin/activate # Mac/Linux
venv\Scripts\activate # Windows
```
### **3️⃣ Install Dependencies**
```bash
pip install -r requirements.txt
```
### **4️⃣ Download the Dataset**
- This project uses the **Fake News Dataset** from [Kaggle](https://www.kaggle.com/c/fake-news/data).
- Place the `train.csv`, `test.csv`, and `submit.csv` files inside the **`data/`** folder.
---
## 📂 Project Structure
```
fake-news-detection/
│── README.md # Project documentation
│── LICENSE # Open-source license (MIT)
│── .gitignore # Ignore unnecessary files
│── requirements.txt # Dependencies
│── setup.py # Setup file (if needed)
│
├── data/ # Dataset files
│ ├── train.csv
│ ├── test.csv
│ ├── submit.csv
│
├── notebooks/ # Jupyter Notebooks
│ ├── eda.ipynb # Exploratory Data Analysis
│
├── models/ # Saved trained models
│
├── src/ # Source code
│ ├── preprocess.py # Data preprocessing script
│ ├── train_model.py # Model training script
│ ├── predict.py # Fake news classification script
│
├── api/ # API backend (Flask or FastAPI)
│
├── frontend/ # UI for interacting with the model
│
└── tests/ # Testing scripts
```
---
## 🔬 Running the Project
### **1️⃣ Run Exploratory Data Analysis**
View dataset statistics and visualizations using **Jupyter Notebook**:
```bash
jupyter notebook notebooks/eda.ipynb
```
### **2️⃣ Train the Model**
Train a machine learning model on the dataset:
```bash
python src/train_model.py
```
### **3️⃣ Make Predictions**
Use the trained model to classify news articles:
```bash
python src/predict.py --text "Breaking news: AI can now detect fake news!"
```
### **4️⃣ (Optional) Run the API**
To serve the model using an API (Flask/FastAPI), navigate to the `api/` folder and run:
```bash
python api/app.py
```
---
## 🛠️ Technology Stack
- **Programming Language** → Python 🐍
- **Libraries** → Pandas, NLTK, Scikit-Learn, Matplotlib, Seaborn
- **Machine Learning** → Logistic Regression, Naive Bayes, SVM (Optional)
- **Text Processing** → TF-IDF Vectorization, Tokenization, Stopword Removal
- **API (Planned)** → Flask or FastAPI
- **Frontend (Planned)** → Streamlit or React
---
## 🚀 Future Improvements
- ✅ **Deploy API for Predictions** (Flask/FastAPI)
- ✅ **Build a Web UI** (Streamlit/React)
- ✅ **Deep Learning Models** (LSTMs, Transformers)
- ✅ **Optimize Accuracy** with Hyperparameter Tuning
---
## 👨💻 Contributing
Contributions are welcome! Feel free to submit a **pull request** or open an **issue**.
### **🔹 Steps to Contribute**
1. Fork the repo.
2. Create a feature branch (`git checkout -b feature-name`).
3. Commit your changes (`git commit -m "Added new feature"`).
4. Push to the branch (`git push origin feature-name`).
5. Create a pull request.
---
## 📜 License
This project is licensed under the **MIT License**.
---
## 📞 Contact
👤 **[Albert F Montoya Jr]**
🔗 **[twitter.com/montoyamedia]**
📧 **[albert@montoyamedia.com]**
---