Spam Message Identification Using Machine Learning Approach

Spam message detection has become an essential task in modern digital communication systems due to the exponential growth of unsolicited messages. Traditional rule-based spam filters, though initially effective, have become inadequate in handling the sophisticated techniques employed by spammers. Consequently, machine learning algorithms are used for spam detection because of their ability to learn the data and these algorithms can improve their performance by providing new data. This research paper explores various ML algorithms utilized for spam detection. Supervised learning methods are predominantly highlighted, focusing on how these models are trained using labeled datasets where messages are classified as spam or non-spam. Additionally, the paper addresses challenges such as the evolving nature of spam, imbalanced datasets, and the need for real-time detection solutions. The paper concludes by exploring the outcome results of ML models for spam detection with Support Vector Classifier (SVC) as best ML model with 97.48% accuracy.