Email Classification Project Guide

Handling a large volume of emails manually is tedious and time-consuming. Important emails might get buried among spam, promotional offers, newsletters, and notifications. Automating email classification helps organize inboxes, prioritize important messages, and filter out irrelevant content. Email classification models use NLP and machine learning to categorize emails intelligently based on their subject, content, and sender patterns.

By using text classification algorithms, you can automatically label incoming emails into categories like Personal, Work, Promotions, Spam, or Social. Pre-trained models like BERT or simple Naive Bayes classifiers can be trained to predict the intent of an email based on its text features. The system can also perform spam detection, helping users declutter their inboxes and ensure they don't miss important communication.

The system receives raw email text (subject + body), processes it through a series of text preprocessing steps (tokenization, lemmatization), and feeds it into a classification model. Based on learned patterns, the model predicts the category (e.g., Spam, Promotion, Personal). Feature extraction techniques like TF-IDF, Word Embeddings, or transformer embeddings improve model understanding. Post-processing sorts or tags the emails into respective folders automatically.

• Collect email datasets like Enron Email Dataset, SpamAssassin, or create a custom labeled email dataset.
• Preprocess: clean HTML tags, extract subject and body, tokenize, remove stopwords, and normalize text.
• Train machine learning models like Naive Bayes, Logistic Regression, or fine-tune transformers like BERT for email classification.
• Evaluate using accuracy, precision, recall, F1-score, and confusion matrices to ensure reliable sorting.
• Deploy the classifier into a web dashboard, email client extension, or server-based auto-sorting pipeline.

• Enron Email Dataset
• SpamAssassin Public Corpus
• Text Classification Using scikit-learn