3 Introduction Spam emails are unsolicited and often malicious emails that flood users' inboxes. This project aims to build a spam email detector using machine learning algorithms. It preprocesses text data, trains a model using a dataset of labeled emails, and deploys a web interface for users to classify emails as spam or non-spam.

Web Application: Flask-based frontend for users to input email text and receive classification results. Machine Learning Model: Trained using a dataset of labeled spam and non-spam emails. Preprocessing: Text data preprocessing includes removing stopwords, tokenization, and TF-IDF vectorization. Evaluation: Model performance metrics include accuracy(0.96), precision, recall, and F1-score.

Enter email text into the provided text area. Click on the "Predict" button to classify the email as spam or non-spam.

The model is trained using the Multinomial Naive Bayes classifier on a dataset of labeled emails. Training includes text preprocessing (removing punctuation, stopwords, and vectorization using TF-IDF). 3 Files Included mail.py: Flask web application for email classification. spam_detector_model.pkl: Trained model saved using joblib. tfidf_vectorizer.pkl: TF-IDF vectorizer for text preprocessing. templates/index.html: HTML template for the frontend.

Python Flask scikit-learn (sklearn) HTML/CSS