To build a Java-based system that automatically detects and filters out spam emails using text classification and machine learning techniques. The system classifies emails as either Spam or Not Spam (Ham) based on the content.**

????️ Technologies Used:

• Programming Language: Java
• Machine Learning Library: Weka (for ML algorithms in Java)
• Dataset: Pre-labeled email datasets (e.g., Enron or SpamAssassin)
• Algorithms: Naive Bayes (commonly used for text classification), or Decision Tree
• GUI (optional): Java Swing for user interface

???? Key Features:

1.Email Text Preprocessing:

Removal of stop words, punctuation, HTML tags

Tokenization (breaking email into words)

2.Feature Extraction:

Convert email content to numerical format using Bag of Words or TF-IDF

Identify keywords, frequency of spam-related terms

3.Model Training & Testing:

Train the model with labeled spam/ham emails

Test with new emails to evaluate accuracy

4.Email Classification:

Input email text → processed → classified as Spam or Not Spam

Output shown in console or GUI

5.User Interface (optional):

Input email content manually

Display prediction result with accuracy

⚙️ How It Works (Workflow):

• Load dataset and preprocess emails.
• Extract features from each email.
• Train the model using Weka in Java.
• Input a new email (from GUI or file).
• The model classifies the email as Spam or Not Spam.
• Show the result to the user.

✅ Benefits:

1. Helps prevent phishing, scams, and junk mail.
2. Automates email filtering, increasing productivity.
3. Lightweight and customizable Java-based tool.

???? Modules Overview:

• Preprocessing Module – Cleans and prepares email content
• Training Module – Trains ML model using Weka
• Classification Module – Classifies emails using trained model
• User Interface – (optional) for input/output interaction