Project Description:

The AI-Based Spam Email Classifier is a machine learning web application that automatically detects and classifies emails as either "Spam" or "Not Spam" (Ham) based on their content. It uses Natural Language Processing (NLP) techniques to analyze the email text and applies a trained ML model to predict its category.

This system is especially useful for email service providers, businesses, or even students wanting to learn text classification, email filtering, and NLP in AI/ML.

Core Objective:

To build a web-based AI system that processes incoming email content and predicts whether it is spam or legitimate, helping users keep their inboxes clean and secure.

Key Features:

1.  Email Input Interface:

   • Users can paste or upload sample email content (subject + body) via web form.

2.  Spam Classification:

   • Uses a trained machine learning model to classify email as "Spam" or "Not Spam".

3.  Confidence Score:

   • Displays probability or confidence level of prediction (e.g., 92% Spam).

4.  Dataset Integration:

   • Uses popular spam datasets like SpamAssassin, Enron Spam Dataset, or SMS Spam Collection for training.

5.  Visualization Module (Optional):

   • Word frequency charts, word clouds of common spam keywords, etc.

6.  Spam Word Highlighting (Optional):

   • Highlights spammy keywords in red inside the email body.

Tech Stack:

 Backend (Choose One):

• PHP / Java / Node.js

• Integrates with Python ML model via API (Flask/Django microservice)

 AI/ML:

• Python (for model training)

  • Libraries: scikit-learn, NLTK, pandas, NumPy

• Algorithms: Naive Bayes, Logistic Regression, or SVM

• Text preprocessing: tokenization, stop-word removal, stemming

 Frontend:

• HTML, CSS, Bootstrap

• JavaScript for dynamic interaction

 Database (Optional):

• MySQL or MongoDB for storing email classification history