Fake News Detection using Python Machine Learning (ML)

“Fake News Detection System Project using Python ML” is a web-based application aimed at combating the growing challenge of online misinformation by combining machine learning techniques with a Django-based web platform. Fake News Detection system is designed to automatically identify whether a news article is genuine or fake using machine learning techniques. Instead of relying on manual fact-checking, which is slow and limited, the system provides a scalable and efficient solution capable of analyzing large volumes of text in real time, Download Fake News Detection System Project with source code nad Project Report for final year Students

Tech Stack Used

Frontend / Web Interface:

• Django (Python Web Framework) – Used to create the web interface for user input, displaying predictions, and managing data
• HTML5, CSS3, JavaScript – For rendering and styling web pages
• Bootstrap (optional) – For responsive UI components
• Django Templates – For dynamic web page rendering

Machine Learning / Backend Logic:

• scikit-learn – Machine Learning library used to implement algorithms like Logistic Regression, Decision Tree, Random Forest, KNN
• NumPy – For numerical operations and matrix manipulation
• Pandas – For handling and preprocessing datasets
• joblib – To save and load the trained machine learning model

Database:

• SQLite – Lightweight relational database used to store user data and predictions
• Django ORM (Object Relational Mapper) – Handles interaction between Django models and the SQLite database

Tools & Environment:

• Python 3.x – Core programming language used
• PyCharm – IDE for development
• Virtualenv / pip – For managing dependencies

Key Features

User Module

1. User Registration & Authentication – Secure login and registration system to allow users to manage their detection history.
2. News Input Interface– Users can enter news article for verifications.
3. Preprocessing & Feature Extraction– Cleans text, removes stop words, and applies vectorization for accurate analysis.
4. Machine Learning Model Integration– Trained with algorithms like Logistic Regression, Random Forest, and Gradient Boosting to achieve over 95% accuracy.
5. Result Visualization– Displays classification results (Real or Fake) along with probability scores.
6. Prediction History– tores past detection results for each user in the database.
7. Scalable Architecture-Built on Django framework with support for SQLite/MySQL databases
8. User-Friendly Interface-Responsive frontend using HTML, CSS, JavaScript, and Django templates.

Admin Module

1. Dashboard – Administrators can view all important system statistics at a glance.
2. User Management:  Admin can view, manage, and control all registered users.
3. Prediction History:  Admin can track which news items were classified as real or fake.
4. Reports & Analytics: Provides summarized reports of news classifications.
5. Role-Based Access: Only authorized administrators can access this panel.
6. User-Friendly Interface: Simple and clean dashboard layout and Easy navigation with sidebar options (Dashboard, Manage Users, Prediction History, Report).
7. Real-Time Updates: System stats update dynamically as users submit new news items for prediction.

Fake News Detection System: Output Screens

Home Page

User Registration

Users’ Fake News Detection History

Admin Dashboard

Manage Registered Users

News Detection History

How to run the Fake News Detection System Python ML Project

1. Download the zip file

2. Extract the file, copy fake_news_project, the folder and paste it on the desktop

3. Open PyCharm and import the project into PyCharm

4. Install four libraries (if not installed)

pip install joblib
pip install numpy
pip install scikit-learn
pip install pandas

5. Run the Project using the following command

python manage.py runserver

Now, click the URL http://127.0.0.1:800,0 and the Project will run

Login Details

*************admin************

Username:  admin

Password: Test@123

*************User************

Username:  john12

Password: Test@123

Or Register a new user.

Fake News Detection System: Project Demo

Fake News Detection System: Project, Report and PPT

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