Fake News Detection System Using Django and Machine Learning Source Code ( Final Year)
Download clean, well-commented Fake News Detection System Using Django and Machine Learning source code for final year projects — easy to run, demo-ready, and mentor-friendly.
- MACHINE-LEARNING Project
- MySQL / MongoDB
- Setup guide & demo steps
- Beginner-friendly
Keywords: source code, final year project code, Fake News Detection System Using Django and Machine Learning Git, documentation, installation guide, machine-learning project, college project demo.
Download Source Code
Admin Features
- Django admin panel for staff and superusers
- Manage all prediction results across all users
- Search, filter, edit, and delete detection records
- Filter records by user, input type, prediction result, and date
- Staff dashboard with full cross-user visibility
- View all users’ history, reports, and dashboard statistics
- Filter reports by user ID and other criteria
- Delete any visible record from staff queryset
- Management commands for model training, demo data seeding, migrations, and superuser creation
Description
Fake News Detection is a Django web application that classifies news articles as Real or Fake using a hybrid machine learning model. The system uses TF-IDF vectorization with Logistic Regression and Random Forest for accurate fake news classification. Users can register, log in, check news by pasting text or article URLs, and view prediction history, analytics, and dashboard reports. The application also supports URL-based article extraction using BeautifulSoup and requests, model transparency with probability scores, admin management tools, staff dashboard access, and report visualization with charts. This project is ideal for demonstrating Django, machine learning, NLP, fake news detection, user authentication, admin panel development, and dashboard analytics in one complete web application
The Fake News Detection System Using Django and Machine Learning final-year Fake News Detection System Using Django and Machine Learning source code is structured for fast setup and easy customization. You get readable code, clear folder architecture, and a guided README so you can run locally and present confidently.
Source Code Overview
Technical snapshot & environment- Project Name
- Fake News Detection System Using Django and Machine Learning
- Language / Stack
- machine-learning
- Database
- MySQL or MongoDB
- Browsers
- Chrome, Firefox, Edge, Opera
- Included in the download
- Frontend,Backend,Database
- Run Scripts
- Documented in README (install, seed, start)
- License
- Academic use for college submission
User Features
- User registration with email and password
- Secure login and logout system
- Password reset with email support
- Fake news detection from pasted news text
- Fake news detection from article URL
- Hybrid prediction using Logistic Regression and Random Forest
- Confidence score and per-model probability display
- Cleaned text preview for transparency
- Optional country, India state, and news category selection
- Personal dashboard with prediction history
- Search and filter previous detection records
- Delete own prediction records
- Reports page with charts and analytics
- Static About and Contact pages
- Health check endpoint for application readiness
Other Features
- Hybrid machine learning pipeline with ensemble probability averaging
- TF-IDF text vectorization for feature extraction
- NLP preprocessing using NLTK stopwords and lemmatization
- URL article extraction using requests, BeautifulSoup, and lxml
- URL authenticity heuristics for additional analysis
- Country hint detection from URLs for reporting
- Model artifact saving using joblib
- Dashboard charts using matplotlib and seaborn
- CSV-based training support for custom datasets
- Dummy data generation using seed command
- Environment variable support with python-dotenv
- User-wise access control for privacy and security
- Staff-level reporting and management system
- Ready for local deployment and further production setup
How to run Fake News Detection System Using Django and Machine Learning
- Create a virtual environment
python -m venv .venv - Activate the virtual environment
Windows:.venv\Scripts\activate
Linux/macOS:source .venv/bin/activate - Install dependencies
pip install -r requirements.txt - Apply migrations
python manage.py migrate - Train the machine learning model
Demo dataset:
python manage.py train_model --csv data/sample_news.csv
Full dataset:
python manage.py train_model - Optional: seed demo data
python manage.py seed_data
or
python manage.py seed_data --user YOUR_USERNAME - Create admin account
python manage.py createsuperuser - Run the development server
python manage.py runserver - Open in browser
Home:http://127.0.0.1:8000/
Register:http://127.0.0.1:8000/register/
Login:http://127.0.0.1:8000/login/
Dashboard:http://127.0.0.1:8000/dashboard/
Admin:http://127.0.0.1:8000/admin/
Credentials
This project does not include default login credentials.
- Create admin account using:
python manage.py createsuperuser - Normal users can register from:
/register/
License
Fake News Detection System Using Django and Machine Learning Source Code Tags
Related & Popular Project Reports
Explore more final-year projects you might be interested in
Concrete Crack Detection Using Machine Learning — Source Code
This project leverages computer vision and machine learning techniques to automate the process of detecting cracks in concrete structures. The primary goal is to provide an efficient and accurate method for damage surveillance in buildings, which is crucial for maintaining structural integrity and safety. The project was developed as an entry for the "PS-1, Concrete Crack Detection". The model has achieved an impressive F1 score of 1, indicating its high accuracy in distinguishing between cracked and non-cracked surfaces.
Brain Tumor Detection Using Machine Learning — Source Code
Brain Tumor Detection Using Machine Learning Source Code project implements a U-Net model to segment brain tumors in MRI images. It focuses on identifying and classifying three types of brain tumors: meningioma, glioma, and pituitary tumors. The project uses the dataset from publicly available brain tumor segmentation data and applies deep learning techniques to accurately delineate tumor regions from MRI scans.
Mental Health Chatbot Using Machine Learning and Flask — Source Code
<p><strong>MindCare</strong> is a <strong>mental health chatbot web application</strong> built with <strong>Python Flask and Machine Learning</strong>. It helps users with <strong>mental wellness support, mood tracking, self-assessments, chat history, and emotional analysis</strong>. The system uses <strong>NLP, TF-IDF, Logistic Regression, and rule-based response selection</strong> to generate chatbot replies from local training data. It also includes a powerful <strong>admin panel</strong> to manage users, chatbot training data, emotion labels, assessments, reports, and wellness content.</p> <p>This <strong>Flask mental health project</strong> is designed for <strong>academic projects, final year projects, portfolio websites, and machine learning demos</strong>. OpenAI integration is optional and can be enabled in code for AI-generated responses.</p>
Dynamic Event Scheduling and Conflict Resolution System — Source Code
FestivalOS is a smart and scalable festival management web application developed using Python, Flask, SQLAlchemy, SQLite, Bootstrap 5, and scikit-learn. The system is designed for large-scale festival operations and helps manage events, venues, resources, participants, and schedule conflicts through an intelligent web platform. This project includes a public landing page, a secure admin console, and a dedicated user portal for attendees, volunteers, performers, coordinators, and administrators. The core highlight of the system is its constraint-aware event scheduling engine, conflict detection module, and machine-learning–assisted priority scoring for better slot recommendations. FestivalOS is an ideal final year college project for students looking to build a real-world event management and scheduling system using Flask and Python.
Agricultural Monitoring and Crop Prediction System with Machine Learning — Source Code
<p><strong>AgriMonitor Pro</strong> is a Flask web application for <strong>smart agricultural monitoring</strong>, <strong>crop recommendation</strong>, <strong>yield prediction</strong>, and <strong>risk classification</strong> using <strong>machine learning</strong>. The system is designed for <strong>farmers</strong> and <strong>administrators</strong> to manage farms, record crop and soil data, train ML models locally, generate predictions, and download reports in <strong>CSV</strong> and <strong>PDF</strong> formats.</p> <p>This agriculture management system uses <strong>Python 3</strong>, <strong>Flask 3</strong>, <strong>SQLAlchemy</strong>, <strong>SQLite</strong>, <strong>pandas</strong>, and <strong>scikit-learn</strong>. It supports <strong>Random Forest classification and regression</strong>, dataset management, user management, farm monitoring, soil health tracking, analytics dashboards, and report generation with <strong>Matplotlib</strong> and <strong>ReportLab</strong>.</p> <p>The platform provides a guided farmer portal for adding farms, entering NPK and weather values, checking crop health, estimating yield, and reviewing prediction history. It also includes a powerful admin panel for managing users, datasets, model training, notifications, feedback, and data exports.</p> <p>This project is suitable for <strong>agriculture technology</strong>, <strong>farm management software</strong>, <strong>smart farming solutions</strong>, <strong>precision agriculture systems</strong>, and <strong>machine learning based crop advisory platforms</strong></p>
Credit Card Fraud Detection Using Machine Learning — Source Code
Fraud Detection model based on anonymized credit card transaction. It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. The datasets contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.
Brain Tumor Detection System using Python and Machine Learning — Source Code
<p><strong>NeuroScan</strong> is a college-level <strong>brain tumor detection web application</strong> built with <strong>Python Flask</strong>, <strong>SQLite</strong>, <strong>scikit-learn</strong>, and <strong>OpenCV</strong>. The system allows users to upload <strong>brain MRI images</strong>, preview scans, run <strong>brain tumor classification</strong>, and download <strong>PDF/TXT reports</strong>. It includes a complete <strong>user panel</strong> and <strong>admin dashboard</strong> with authentication, prediction history, model training, dataset management, reporting, and feedback handling.</p> <p>This project performs <strong>local machine learning inference</strong> without using any third-party API. The ML pipeline uses <strong>HOG features</strong>, <strong>image histogram analysis</strong>, <strong>edge detection</strong>, and a <strong>RandomForestClassifier</strong> for <strong>MRI image classification</strong>. It supports both <strong>binary classification</strong> such as <strong>Tumor / No Tumor</strong> and <strong>multiclass brain tumor classification</strong> depending on the training dataset.</p> <p>The project is ideal for <strong>final year students</strong>, <strong>machine learning beginners</strong>, <strong>Flask web development learners</strong>, and <strong>academic demonstration purposes</strong></p>
Quick actions
What we provide
Project reports, source code, and PPTs tailored for final-year students. Explore, or message us for a custom build.