📰 TruthGuard AI - Fake News Detection System

📌 Overview

TruthGuard AI is an end-to-end Machine Learning application designed to classify news articles as Fake or Real by analyzing textual patterns using Natural Language Processing (NLP).

The system uses Machine Learning techniques to understand linguistic features and writing patterns present in news articles.

The system processes raw news text, extracts meaningful features using TF-IDF Vectorization, and predicts authenticity using a trained Linear Support Vector Machine (SVM) model.

The project includes complete ML workflow:

• Data preprocessing
• Exploratory Data Analysis
• NLP text cleaning
• Feature extraction
• Model training
• Model evaluation
• Deployment-ready Streamlit application

---

🚀 Live Demo

Experience TruthGuard AI here:

🔗 Live Application:

https://fake-news-detection-vk18.streamlit.app/

🔗 GitHub Repository:

https://github.com/deepakjha018/Fake-News-Detection

---

📸 Application Screenshots

🏠 Homepage

✅ Real News Prediction

🚨 Fake News Prediction

---

🧠 Machine Learning Workflow

Raw Dataset
      |
      ↓
Text Cleaning
      |
      ↓
Stopword Removal + Stemming
      |
      ↓
TF-IDF Vectorization
      |
      ↓
Linear SVM Model
      |
      ↓
Fake / Real Prediction

---

📊 Model Performance

Metric	Value
Algorithm	Linear Support Vector Machine
Feature Extraction	TF-IDF
Dataset Size	44,898 Articles
Accuracy	99.36%

---

📁 Project Structure

Fake-News-Detection/

│
├── app/
│   └── app.py
│
├── data/
│
├── models/
│   ├── fake_news_model.pkl
│   └── tfidf_vectorizer.pkl
│
├── notebooks/
│   ├── 01_data_preparation.ipynb
│   ├── 02_data_exploration.ipynb
│   ├── 03_text_preprocessing.ipynb
│   ├── 04_model_training.ipynb
│   └── 05_model_evaluation.ipynb
│
├── reports/
│
├── screenshots/
│
├── src/
│   ├── preprocessing.py
│   ├── predict.py
│   ├── train_model.py
│   ├── evaluate.py
│   └── utils.py
│
├── requirements.txt
├── LICENSE
└── README.md

---

🛠️ Technologies Used

Programming Language

• Python

Libraries

• Pandas
• NumPy
• Scikit-Learn
• NLTK
• Matplotlib
• Seaborn
• WordCloud
• Joblib

Deployment

• Streamlit

---

⚙️ Installation & Usage

1. Clone Repository

git clone https://github.com/deepakjha018/Fake-News-Detection.git

2. Navigate Directory

cd Fake-News-Detection

3. Install Dependencies

pip install -r requirements.txt

4. Run Application

streamlit run app/app.py

---

🔍 Features

✔ Fake News Classification

✔ Real-time Prediction

✔ Confidence Score

✔ Interactive Streamlit UI

✔ Complete NLP Pipeline

✔ Model Evaluation Reports

✔ Production Ready Structure

---

📚 Dataset

Dataset used:

Fake and Real News Dataset

Containing:

• Fake.csv
• True.csv

Total Records:

44,898 news articles

---

---

🌐 Deployment

The application is deployed using Streamlit Cloud.

Deployment Features:

• Interactive Web Interface
• Real-time News Classification
• Cloud Hosted ML Model
• User Friendly Prediction System

---

Future Improvements

• Transformer based models (BERT)
• Multilingual fake news detection
• News URL verification
• Real-time fact checking APIs

---

👨‍💻 Developer

Developed by:

Deepak Kumar Jha

AI & Data Science Student

Passionate about Machine Learning, Artificial Intelligence and Data Science.

---

⭐ If you found this project helpful, consider giving it a star!