01 Source Code Only
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- Complete project source code
- Database / data resources
- Dependencies & configuration
- README / setup guide
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Complete final-year project source code with frontend, backend, database and setup documentation. Instant download after secure payment.
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01 Source Code Only
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02 Code + Setup Support
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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