A UNIFIED APPROACH FOR SPAM DETECTION USING MACHINE LEARNING

Abstract

Spam, uninvited and frequently unrelated communications delivered to many individuals via the
internet, has grown in concern since electronic communication began. Spam began on ARPANET, the
forerunner to the internet, in 1978. This incident started a digital challenge. Spam has increased rapidly, with
estimates putting it at 85% of email traffic. Unwanted email clutters inboxes and creates security issues, since it
typically contains phishing and other fraud. The CAN-SPAM Act of 2003 and advanced spam filters have been
developed to combat spam. Despite these attempts, spammers' ingenuity and perseverance keep spam a
problem.
My research seeks to create a strong spam detection system that uses machine learning techniques to reliably
categorize communications as spam or ham to address this continuous difficulty. Python and Flask are used for
data preparation, feature extraction, model training, and a user-friendly web interface. The project starts with
NLTK tokenization, stemming, and stopword cleansing of message data. To reduce noise and let machine
learning models concentrate on the most informative material, this step is essential. The TF-IDF vectorizer turns
preprocessed text into numbers. This technique represents text data nuancedly by weighting words by frequency
and relevance. Random Forest, Naive Bayes, and XGBoost train on extracted features. Each model has
strengths, from Random Forest's ensemble method to Naive Bayes' probabilistic predictions and XGBoost's
gradient boosting. Models are assessed by their test data classification accuracy. A voting classifier uses all
three models' predictions to classify to improve decision-making.
Users enter messages and obtain quick categorization using a Flask web application. Application features
include user login, message entry, and categorization result display. The system can categorize new messages
efficiently without retraining since pickle saves and loads the learned models and vectorizer. This initiative
advances spam fighting. It combines sophisticated machine learning with practical web development to
recognize and filter spam. Spam detection systems will change as spam does, and this project sets the
framework for them.