Overview:

The Spam Email Classification System is a Machine Learning–based application that automatically detects and filters spam (unwanted) emails from legitimate ones.
The system analyzes the content, subject line, and sender’s metadata of an email using Natural Language Processing (NLP) techniques to identify patterns associated with spam messages such as phishing, advertisements, or scams.

The main goal is to enhance email security and ensure that users only see relevant and trustworthy messages in their inbox.

Objectives:

• To automatically classify emails as “Spam” or “Ham (Not Spam)” using ML models.

• To protect users from phishing, scam, and malicious email attacks.

• To use text mining and NLP to understand email content patterns.

• To achieve high accuracy with minimal false classifications.

Key Features:

1. Email Data Analysis: Processes email text, subject, and sender address for analysis.

2. Machine Learning Classification: Uses trained models to predict whether an email is spam or not.

3. Feature Extraction: Uses NLP techniques (tokenization, stopword removal, stemming, TF-IDF).

4. Accuracy Evaluation: Displays precision, recall, and F1-score metrics for performance.

5. Dataset Integration: Works on benchmark datasets like the Enron Email Dataset or SpamAssassin.

6. Admin Dashboard: Allows dataset upload, model training, and testing new data.

7. Visualization Reports: Shows charts of spam vs. non-spam detection rates.

8. Real-time Detection: Can be integrated with email clients for live spam filtering.

9. Secure Processing: Protects user data while processing email content.

10. Responsive Interface: Simple, user-friendly web dashboard for interaction.

Tech Stack:

• Frontend: HTML, CSS, Bootstrap, JavaScript

• Backend: Python (Flask/Django) / Node.js / PHP

• Database: MySQL / MongoDB

• Machine Learning Libraries:

  • scikit-learn, pandas, NumPy

  • NLP: NLTK / spaCy

  • Algorithms: Naïve Bayes (MultinomialNB), SVM, Logistic Regression

• Tools: Jupyter Notebook, Google Colab (for model training)