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Spam detection mini engine for emails

Why Choose This Project?

Email spam is a major cybersecurity and productivity issue, with millions of spam/phishing emails sent daily. This project aims to build a mini spam detection engine that can classify emails as spam or legitimate using machine learning, NLP (Natural Language Processing), and rule-based filters. It helps organizations or individuals filter out harmful or irrelevant emails before they reach the inbox.

What You Get in This Project

A mini spam detection system that processes raw email text.
ML/NLP-based spam classifier (Naive Bayes / Logistic Regression / Random Forest).
Rule-based filters for blacklisted domains, suspicious keywords, or attachments.
Web-based interface or CLI tool to test emails.
Accuracy reports and confusion matrix for evaluation.

Technology Stack

Layer	Technology
Data	Enron Spam Dataset / SpamAssassin Corpus
Language	Python
ML Models	Naive Bayes, Logistic Regression, Random Forest, SVM
NLP Tools	NLTK, Scikit-learn, spaCy, TF-IDF Vectorizer
Backend (optional)	Flask / Django API for email testing
Frontend (optional)	HTML, CSS, JavaScript for UI
Database (optional)	SQLite / PostgreSQL for storing results

Key Features

Feature	Description
Email Preprocessing	Cleans emails (stopwords removal, stemming, tokenization)
Spam Classification	ML model predicts if email is spam/ham
Keyword & Rule Filters	Detects suspicious patterns like "win money", "lottery", "free gift"
Blacklist Matching	Checks against blacklisted IPs, domains, or senders
Attachment Scanning	Flags malicious file types (e.g., `.exe`, `.js`)
Accuracy Evaluation	Reports precision, recall, F1-score
User Interface	Web app/CLI for entering emails and getting spam score
Confidence Score	Displays spam probability (e.g., 92% spam)

How It Works

1. Data Preprocessing

Load raw email dataset.
Clean subject + body (remove HTML tags, punctuation, stopwords).
Convert text into numerical form using Bag of Words / TF-IDF.

2. Model Training

Train ML models (Naive Bayes, Logistic Regression, Random Forest).
Evaluate using test data → measure accuracy, precision, recall, F1.

3. Spam Detection Workflow

User submits an email text.
Preprocessing + TF-IDF transformation.
ML model assigns spam / not spam label with probability.
Rule-based filters add extra detection (keywords, sender blacklist).

4. Output & Dashboard

Shows spam score (%) and decision (Spam/Ham).
Admin/user can mark false positives/negatives to improve model.

Security Features

Blacklist Integration → Detects known spammer domains/IPs.
Heuristic Rules → Flags emails with phishing-like patterns (too many links, obfuscated text).
Attachment Filtering → Blocks dangerous file types.
Model Retraining → Continuously improves detection with new spam samples.
Explainability → Highlights which words/phrases triggered spam detection.