Spam detection is a critical task in the modern digital world, where the volume of unsolicited and potentially harmful messages has grown exponentially. This project aims to develop a spam detection system using machine learning techniques to automatically classify messages as either spam or non-spam (ham).

The system utilizes various machine learning algorithms, including Naive Bayes, Support Vector Machines (SVM), and Random Forests, to identify patterns in text data and distinguish between legitimate and spam content. Feature extraction techniques, such as term frequency-inverse document frequency (TF-IDF) and bag-of-words (BoW), are employed to convert textual information into numerical representations that can be processed by machine learning models.

The project also explores the use of natural language processing (NLP) techniques for preprocessing, such as tokenization, stemming, and stop word removal, to enhance the accuracy of classification. The performance of the models is evaluated using standard metrics such as accuracy, precision, recall, and F1-score, with an emphasis on achieving a balance between false positives and false negatives. The proposed system demonstrates promising results in detecting spam across various datasets and offers potential for real-world applications in email filtering, messaging platforms, and cybersecurity.