📧 Email Spam Classifier

A machine learning project that classifies emails as Spam or Not Spam using Google's Word2Vec embeddings and a Random Forest classifier — implemented entirely in a Jupyter Notebook.

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🧠 Overview

This project explores a more context-aware approach to spam detection. Instead of using TF-IDF or Bag-of-Words, it leverages semantic word embeddings from Google’s Word2Vec model, combined with a Random Forest algorithm to classify emails.

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🚀 Features

• Implemented in a single Jupyter Notebook
• NLP-based text preprocessing
• Google Word2Vec (pre-trained) for word embeddings
• Vector averaging to represent entire emails
• Random Forest Classifier for prediction
• Evaluation metrics like accuracy, precision, recall, and F1-score

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🛠️ Technologies Used

• Python 3.x
• Jupyter Notebook
• NLTK (text cleaning, stopword removal)
• Gensim (Word2Vec model loading)
• Scikit-learn (RandomForestClassifier & evaluation)

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🔁 Workflow

1. Text Preprocessing
   • Lowercase conversion
   • Tokenization
   • Stopword removal
2. Embedding
   • Load Google's GoogleNews-vectors-negative300 Word2Vec model
   • Convert each email into a 300-d vector by averaging word vectors
3. Classification
   • Train a Random Forest classifier
   • Use cross-validation & metrics to evaluate performance
4. Prediction
   • Input: Raw email text
   • Output: Spam or Not Spam

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