Spam Email Detection System

Python NLP Multinomial Naive Bayes TF-IDF Scikit-learn

Project Overview

This project is an intelligent system that uses Natural Language Processing (NLP) to detect and classify spam emails[cite: 25]. By leveraging the Multinomial Naive Bayes machine learning model, the system is able to achieve a classification accuracy of over 95%. It features a complete pipeline from data preprocessing to model evaluation, providing a practical solution to a common real-world problem[cite: 27, 28].

Key Features

Advanced Text Preprocessing: Implemented a comprehensive text cleaning pipeline that includes tokenization, stemming, and the removal of stopwords and punctuation to improve model accuracy.
TF-IDF Vectorization: Applied Term Frequency-Inverse Document Frequency (TF-IDF) to convert textual data into a meaningful numerical representation for the machine learning model.
Interactive CLI: Developed a command-line interface that allows a user to input email text and receive an instant spam/not-spam classification.
Performance Evaluation: The model's effectiveness was thoroughly validated using key metrics such as precision, recall, and accuracy to ensure reliable performance.

Technical Insights

The success of this project hinged on effective feature extraction. Using TF-IDF was critical as it gives more weight to words that are significant to a specific email, rather than just common words. This experience provided a deep understanding of the NLP workflow and demonstrated how a relatively simple yet powerful algorithm like Multinomial Naive Bayes can yield excellent results in text classification tasks when paired with robust data preprocessing techniques.