FakeNews AI

AI-Powered Fake News Detection System

Detect whether a news article is Real or Fake using Natural Language Processing (NLP) and Machine Learning with a modern Flask web interface.

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Overview

FakeNews AI is a machine learning web application that classifies news articles into Real or Fake using TF-IDF Vectorization and a Logistic Regression classifier.

The application provides an intuitive interface where users can paste any news article and instantly receive:

• Prediction (Real/Fake)
• Confidence Score
• Prediction History
• Fast Response Time
• Interactive UI

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Features

• AI-powered Fake News Detection
• News Article Classification
• Confidence Score
• Real-Time Prediction
• TF-IDF Feature Extraction
• Logistic Regression Classifier
• Text Cleaning using NLTK
• Local Prediction History
• Dark / Light Theme
• Fully Responsive Design

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System Architecture

flowchart LR

A[User] --> B[Flask Web Application]

B --> C[Input News Article]

C --> D[Text Preprocessing]

D --> E[TF-IDF Vectorizer]

E --> F[Logistic Regression Model]

F --> G[Prediction]

G --> H[Confidence Score]

H --> I[Display Result on UI]

Loading

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Workflow

User
   │
   ▼
Enter News Article
   │
   ▼
Text Cleaning (NLTK)
   │
   ▼
TF-IDF Feature Extraction
   │
   ▼
Logistic Regression Prediction
   │
   ▼
Confidence Score Calculation
   │
   ▼
Display Result

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Project Structure

FakeNews-AI/
│
├── app.py
├── requirements.txt
├── README.md
│
├── dataset/
│   └── news_data.csv
│
├── models/
│   └── fake_news_model.pkl
│
├── src/
│   ├── preprocess.py
│   └── train_model.py
│
├── static/
│   ├── css/
│   ├── js/
│   └── images/
│
├── templates/
│   └── index.html
│
└── screenshots/

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Machine Learning Pipeline

Text Preprocessing

• Lowercase Conversion
• Remove Punctuation
• Remove Numbers
• Remove Stopwords
• Clean Text

↓

Feature Engineering

TF-IDF Vectorization

↓

Classification

Logistic Regression

↓

Prediction

Real News / Fake News

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Technologies Used

Technology	Purpose
Python	Backend
Flask	Web Framework
HTML5	Frontend
CSS3	Styling
JavaScript	Client-side Interaction
Scikit-Learn	Machine Learning
NLTK	Text Preprocessing
Joblib	Model Serialization
TF-IDF	Feature Extraction
Logistic Regression	Classification

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Model Performance

Metric	Value
Algorithm	Logistic Regression
Feature Extraction	TF-IDF
Accuracy	97.33%
Prediction Speed	~1 Second

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Installation

Clone the repository

git clone https://github.com/MobeenFatimaa/FakeNews-Detection-Application

Go to the project

cd FakeNews-AI

Create virtual environment

python -m venv .venv

Activate

Windows

.venv\Scripts\activate

Linux/Mac

source .venv/bin/activate

Install dependencies

pip install -r requirements.txt

Run the application

python app.py

Open

http://127.0.0.1:5000

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User Interface

The application provides:

• Modern Landing Page
• News Analyzer
• AI Loading Animation
• Result Card
• Confidence Meter
• Prediction History
• Statistics Section
• Responsive Design

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Future Improvements

• BERT / DistilBERT Integration
• Fake News Source Detection
• Explainable AI (XAI)
• Multilingual News Detection
• User Authentication
• Cloud Deployment
• News URL Analysis
• Live News API Integration

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Kaggle Notebook

View the complete machine learning notebook on Kaggle:

(https://www.kaggle.com/code/mobeenfatimah/fake-news-detection-97-1-accuracy)

Developer

Mobeen Fatima

Computer Science Student

Machine Learning & AI Enthusiast

GitHub: https://github.com/MobeenFatimaa

LinkedIn: https://www.linkedin.com/in/mobeen-fatima-599a35347/

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Support

If you found this project useful, consider giving it a ⭐ on GitHub.

It helps others discover the project and motivates future improvements.

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License

This project is licensed under the MIT License.