01 Synopsis
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- PDF & Word both included
- Up to 30 pages
- Only 1 diagram included
- Problem statement & objectives
- Ready for college submission
Complete final-year project report with documentation, diagrams and viva-ready structure. Instant PDF & Word download — plagiarism-free and faculty-aligned.
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01 Synopsis
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02 Pre Defined Project Report
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03 Customized Report
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04 Originality Reviewed
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Abstract
Table of Content
Introduction
Problem Statement
Existing System
Proposed System
Objectives
System Architecture
Major Functional Modules
Hardware Requirements
Software Requirements
Future Enhancement
Conclusion
References
Abstract
Table of Content
Chapter 1 — Introduction
Chapter 2 — Literature Review / System Study
Chapter 3 — System Analysis
Chapter 4 — System Design
Chapter 5 — System Implementation
Chapter 6 — Testing
Chapter 7 — Results and Discussion
Chapter 8 — Conclusion and Future Enhancements
Chapter 9 — References
Email Spam Detection System is a Python Flask based final year project developed for classifying email-like text as Spam or Ham using machine learning. This major project includes user authentication, user dashboard, spam prediction, prediction history, statistics, profile management, export options, admin dashboard, dataset upload, model training, metrics, confusion matrix, and global prediction management. Users can sign up, log in, analyze email subject and body, view spam/ham prediction result, check confidence score, save prediction history, search and filter records, delete history rows, view dataset information, update profile, change password, and export history as CSV or text. Admin users can manage users, view all predictions, upload datasets, train models, evaluate accuracy, precision, recall, F1 score, and view training history. This email spam detection source code is suitable for students who need a final year project, major project, minor project, source code, and project report based on Flask, SQLite, NLP, and machine learning.
Administrator Account
[email protected]Adminpass1/auth/admin_loginDemo User Accounts
All demo users use password:
Userpass1
Demo users:
[email protected][email protected][email protected][email protected][email protected][email protected][email protected]User Login:
/auth/login
/admin/*data//app/*Model artifacts saved under:
model_artifacts/
Open the project folder that contains:
run.py
requirements.txt
Create virtual environment:
python -m venv .venv
Activate virtual environment on Windows:
.\.venv\Scripts\Activate.ps1
Install dependencies:
python -m pip install -r requirements.txt
Seed the database:
python seed.py
Optional: download Kaggle dataset:
python download_kaggle_dataset.py
Start the application:
python run.py
Open project in browser:
http://127.0.0.1:5000
Open user login:
/auth/login
/auth/admin_login
Start the Flask app:
python run.py
Open admin login:
http://127.0.0.1:5000/auth/admin_login
Open Dataset & Training:
http://127.0.0.1:5000/admin/dataset
Select sample dataset:
sample_sms_demo.csv
Click:
Train & evaluate
Administrator Account
[email protected]Adminpass1/auth/admin_loginDemo User Accounts
All demo users use password:
Userpass1
Demo users:
[email protected][email protected][email protected][email protected][email protected][email protected][email protected]User Login:
/auth/login