Fake News Detection

Overview

This project aims to detect fake news using machine learning techniques. The model is trained on a dataset containing both fake and real news articles and classifies news articles accordingly.

Project Tasks

1. Exploratory Data Analysis (EDA)

• Perform EDA on the dataset to uncover trends, patterns, and actionable insights.
• Deliverables:
  • Python code (Jupyter Notebook)
  • PDF report summarizing 5 key insights.

2. Fake News Classification Model

• Build a machine learning model to classify news as real or fake.
• Deliverables:
  • Trained model file.
  • Python code (Jupyter Notebook) for model development.
  • Performance evaluation metrics report.

3. Web Interface (Optional)

• Develop a simple web interface to allow users to check news authenticity.
• Deliverables:
  • Flask/Django web app.
  • Deployment guide.

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Dataset

The project uses a dataset containing:

1. News Content:
   • Title: Headline of the news article.
   • Text: Full content of the news article.
   • Label: Classification label (Fake or Real).

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Repository Structure

├── data/
│   ├── fake_news_dataset.csv
├── notebooks/
│   ├── EDA.ipynb
│   ├── Model_Training.ipynb
├── reports/
│   ├── EDA_Report.pdf
│   ├── Model_Performance.pdf
├── webapp/
│   ├── app.py
│   ├── templates/
├── README.md

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Getting Started

Prerequisites

• Python 3.7+
• Required libraries:
  • pandas
  • numpy
  • matplotlib
  • seaborn
  • scikit-learn
  • nltk
  • Flask/Django (if deploying a web app)

Installation

1. Clone the repository:

   git clone https://github.com/<username>/fake-news-detection.git
   cd fake-news-detection

2. Install required libraries:

   pip install -r requirements.txt

3. Upload the dataset to the data/ directory.

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Usage

Run EDA:

1. Navigate to the notebooks/ directory.
2. Open and execute the EDA.ipynb notebook to perform EDA and generate insights.

Train Fake News Detection Model:

1. Open and execute the Model_Training.ipynb notebook.
2. Review the model performance report in reports/.

Deploy Web Interface (Optional):

1. Navigate to the webapp/ directory.
2. Run the web application:

   python app.py

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Results

• EDA: Identified key trends in fake and real news articles.
• Model: Achieved high accuracy in classifying fake news.
• Web Interface (Optional): Allows users to check news authenticity in real-time.

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Author

Kanishkar V
kanishvijay2005@gmail.com
www.linkedin.com/in/kanishkar-v-3471782a2/

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License

This project is licensed under the MIT License. See the LICENSE file for details.