A machine learning-based email spam classifier that identifies and filters out spam emails from legitimate ones. This project uses Natural Language Processing (NLP) techniques and machine learning algorithms to classify emails as "spam" or "ham" (not spam) based on their content.
🔍 Project Overview
This Email Spam Classifier is built with the goal of automatically detecting spam emails to reduce inbox clutter and enhance email security. The model is trained on a labeled dataset of emails, with features extracted using techniques like TF-IDF vectorization. A variety of machine learning algorithms, such as Naive Bayes and Support Vector Machines (SVM), can be used to classify the emails.
✨ Features
Preprocesses email data by removing stop words, punctuation, and irrelevant content. Extracts features using TF-IDF vectorization. Trains and tests different classifiers (e.g., Naive Bayes, SVM) for the best performance. Evaluates model performance with metrics such as accuracy, precision, recall, and F1-score. 📁 Dataset
The model can be trained on a variety of datasets, such as:
SMS Spam Collection Dataset Enron Email Dataset SpamAssassin Public Corpus Note: Ensure your dataset is labeled with spam and ham (not spam) tags for training.