An Email Spam Classifier project, helps you detect your spam email from correct email. Try it out here!

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Enhanced email spam classifier using a TF-IDF vectorizer and XGBoost. Includes improved text cleaning, n-gram features, and tuned model parameters. Performs stratified cross-validation with accuracy, precision, recall, and F1 reporting. Saves model and evaluation report for easy reuse and prediction.

A basic NLP classification task to classify between spam and ham on a given Email spam dataset of 5,574 emails. The files contain one message per line. Each line is composed by two columns: v1 contains the label (ham or spam) and v2 contains the raw text.