01 Synopsis
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B.Tech Project Report
B.Tech Fake News Detection System Using Django and Machine Learning Report
Complete B.Tech final-year project report with documentation, diagrams and viva-ready structure. Instant PDF & Word download — plagiarism-free and faculty-aligned.
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02 Pre Defined Project Report
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Abstract
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Table of Content
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01
Introduction
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02
Problem Statement
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03
Existing System
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04
Proposed System
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05
Objectives
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06
System Architecture
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07
Major Functional Modules
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08
Hardware Requirements
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09
Software Requirements
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10
Future Enhancement
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11
Conclusion
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12
References
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—
Abstract
-
—
Table of Content
-
01
Chapter 1 — Introduction
- 1.1 Background of the Project
- 1.2 Problem Statement
- 1.3 Objectives of the System
- 1.4 Scope of the Project
- 1.5 Existing System Overview
- 1.6 Proposed System Overview
- 1.7 Technologies Used
- 1.8 Overview
- 1.9 Limitations
- 1.10 Advantages
- 1.11 Disadvantages
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02
Chapter 2 — Literature Review / System Study
- 2.1 Introduction
- 2.2 Review of Similar Systems
- 2.3 Comparative Analysis
- 2.4 Software Development Models
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03
Chapter 3 — System Analysis
- 3.1 Introduction
- 3.2 Functional Requirements
- 3.3 Non-Functional Requirements
- 3.4 User Requirements
- 3.5 Feasibility Study
- 3.5.1 Technical Feasibility
- 3.5.2 Economic Feasibility
- 3.5.3 Operational Feasibility
- 3.6 System Architecture
- 3.7 Data Flow Diagram (Level 0)
- 3.8 Data Flow Diagram (Level 1)
- 3.9 Data Flow Diagram (Level 2)
-
04
Chapter 4 — System Design
- 4.1 Introduction
- 4.2 Use Case Diagram
- 4.3 Class Diagram
- 4.4 Sequence Diagram
- 4.5 Activity Diagram
- 4.6 ER Diagram
- 4.7 Database Schema
- 4.8 Table Structures
-
05
Chapter 5 — System Implementation
- 5.1 Introduction
- 5.2 Development Environment
- 5.3 Tools and Technologies Used
- 5.4 Hardware Requirements
- 5.5 Software Requirements
- 5.6 Module-wise Implementation
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06
Chapter 6 — Testing
- 6.1 Introduction
- 6.2 Testing Strategy
- 6.3 Unit Testing
- 6.4 Integration Testing
- 6.5 System Testing
- 6.6 Test Cases
- 6.7 Bug Reports
-
07
Chapter 7 — Results and Discussion
- All screenshots
-
08
Chapter 8 — Conclusion and Future Enhancements
- 8.1 Conclusion
- 8.2 Future Enhancements
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09
Chapter 9 — References
Project's Overview
Fake News Detection System Using Django and Machine Learning is a web-based final year project developed for classifying news as Real or Fake using a hybrid machine-learning pipeline. This major project allows users to register, log in, paste news text, submit article URLs, run fake news detection, view prediction labels, check confidence scores, review hybrid and per-model probabilities, access dashboard analytics, search detection history, delete own records, and generate visual reports. The system uses TF-IDF vectorization, Logistic Regression, Random Forest, NLTK preprocessing, and hybrid ensemble probability averaging for prediction. Staff users can view cross-user data, manage detection records, filter reports, and access extra staff-level analytics. Django admin provides complete management of prediction results. This fake news detection source code is suitable for students who need a final year project, major project, minor project, source code, and project report based on Django, Python, NLP, and machine learning.
Login Credentials
This repository does not include default usernames or passwords.
Django Admin / Superuser:
Create locally using:
python manage.py createsuperuser
Then login at:
http://127.0.0.1:8000/admin/
Normal App User:
Register from:
http://127.0.0.1:8000/register/
Password Reset:
Configure SMTP or file-based email backend using .env.example. Real email secrets should not be committed.
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- Staff dashboard with cross-user visibility in this final year project
- Staff overview with all users’ prediction data
- Staff history view with all visible records
- Staff reports with cross-user filters
- Extra User column in staff tables
- Filter reports by user ID
- Staff can delete records visible in their queryset
- Django admin access for superusers/staff
- Manage all prediction results from Django admin
- List prediction entries
- Search prediction entries
- Filter by user
- Filter by input type
- Filter by prediction
- Filter by date
- Add prediction result manually from admin
- Change prediction result from admin
- Delete prediction result from admin
- Standard Django superuser support
- Create superuser using command line
- Management command for model training
- Management command for seed data
- Standard Django migrate command support
- User registration in this final year project
- User login and logout
- Email-based password reset
- Auto login after registration
- Public landing page
- Detection form for logged-in users
- Paste news text for detection
- Submit article URL for detection
- Optional country field
- Optional India state field
- Optional news category field
- Text input validation
- URL article fetching
- HTML extraction using BeautifulSoup
- URL authenticity heuristic support
- Country hint inference from URL
- Fake/Real prediction display
- Confidence score display
- Hybrid ensemble probability display
- Logistic Regression probability display
- Random Forest probability display
- Cleaned-text preview
- URL authenticity information display
- Store each prediction in database
- User dashboard overview
- View total detections
- View fake vs real breakdown
- View model trained/not trained status
- View latest prediction records
- View own prediction history
- Search history by text, URL, prediction, country, state, and category
- Delete own prediction records
- Dashboard reports with charts
- Filter reports by date
- Filter reports by country
- Filter reports by category
- Filter reports by prediction
- Filter reports by input type
- View label distribution chart
- View detections over time
- View category/country breakdown where data exists
- Access About page
- Access Contact page
- Health check endpoint support
Machine Learning Features in this Final Year Project
- Fake news classification using machine learning
- Hybrid ML prediction pipeline
- TF-IDF vectorization
- Logistic Regression model
- Random Forest model
- Probability averaging ensemble
- Fake threshold set at averaged fake probability ≥ 0.4
- Stores per-model probabilities
- Stores hybrid probability
- Trains from labelled CSV dataset
- Supports Kaggle-style
text+labeldataset - Text preprocessing using NLTK
- Stopword removal with negation words retained
- WordNet lemmatization
- Train/test split support
- Accuracy score calculation
- Confusion matrix generation
- Classification report generation
- Model artifacts saved using joblib
-
Saved vectorizer:
ml_artifacts/tfidf.joblib -
Saved Logistic Regression model:
ml_artifacts/logreg.joblib -
Saved Random Forest model:
ml_artifacts/rf.joblib -
Saved metadata:
ml_artifacts/meta.json
URL Detection Features in this Final Year Project
- Article URL input support
- Fetches HTML using requests
- Extracts article text using BeautifulSoup
- Uses lxml parser support
- Validates extracted article length
- Supports fallback by pasting text when URL extraction fails
- URL authenticity heuristic support
- URL country hint support
- Stores URL-based prediction records
- Displays URL authenticity details when available
Dashboard and Report Features in this Final Year Project
- User dashboard overview
- Detection totals
- Fake vs Real distribution
- Latest detection records
- Model training status indicator
- Detection history table
- Search and filter history
- Delete own detections
- Report dashboard with charts
- Label distribution chart
- Detection trend over time
- Country-wise breakdown where data exists
- Category-wise breakdown where data exists
- Input type based filtering
- Prediction based filtering
- Date range filtering
- Staff-level report visibility
- Matplotlib and Seaborn chart generation
Other Features in this Final Year Project
- Complete fake news detection source code
- Suitable for final year project, major project, and minor project
- Project report content can be prepared from included modules
- Django web application
- User authentication system
- Django admin support
- Staff dashboard support
- Text-based fake news detection
- URL-based fake news detection
- NLP preprocessing
- Hybrid machine learning ensemble
- Prediction history storage
- Dashboard analytics
- Reports and visual charts
- Password reset email support
- Email fallback folder support
- Environment configuration using
.env - Health check endpoint
- Management commands included
- Dataset training command included
- Seed data command included
- No default admin shipped in repository
- Useful for viva, source code review, project report writing, and project demonstration
-
Open terminal in the project root folder containing:
manage.py -
Create virtual environment:
python -m venv .venv -
Activate virtual environment on Windows:
.venv\Scripts\activate -
Activate virtual environment on Linux/macOS:
source .venv/bin/activate -
Install dependencies:
pip install -r requirements.txt -
Optional: copy environment file:
.env.example -
Rename or copy it as:
.env -
Run database migrations:
python manage.py migrate -
Place labelled dataset at:
data/news.csv - For quick demo training, run:
-
python manage.py train_model --csv data/sample_news.csv - For full training, run:
-
python manage.py train_model - Optional: insert demo rows:
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python manage.py seed_data - Optional: attach demo rows to a specific registered user:
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python manage.py seed_data --user YOUR_USERNAME - Create Django admin account:
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python manage.py createsuperuser - Start development server:
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python manage.py runserver - Open landing page:
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http://127.0.0.1:8000/ - Open register page:
-
http://127.0.0.1:8000/register/ - Open login page:
-
http://127.0.0.1:8000/login/ - Open dashboard:
-
http://127.0.0.1:8000/dashboard/ - Open Django admin:
-
http://127.0.0.1:8000/admin
README Included
Setup Support Available
This repository does not include default usernames or passwords.
Django Admin / Superuser:
Create locally using:
python manage.py createsuperuser
Then login at:
http://127.0.0.1:8000/admin/
Normal App User:
Register from:
http://127.0.0.1:8000/register/
Password Reset:
Configure SMTP or file-based email backend using .env.example. Real email secrets should not be committed.