Detection of Unsolicited Emails and Texts Using Machine Learning

Spam messages, which include unsolicited emails, text messages, and other forms of unwanted communication, continue to be a persistent problem in the digital world. This work focuses on developing an efficient spam message detection system using machine learning techniques. The primary goal of this present work is to automatically identify and filter out spam messages, reducing the inconvenience and potential harm they cause to users. The present work utilizes a diverse dataset of both spam and non-spam messages to train and evaluate various machine learning models. Key components of the present work include data pre-processing, feature extraction, model training, and evaluation. Natural language processing (NLP) techniques are applied to extract relevant features from text data, enabling the models to learn the patterns and characteristics of spam messages. The present work aims to achieve a high level of accuracy and precision in identifying spam messages while minimizing false positives. Once the model is trained and validated, it can be integrated into email and messaging platforms to automatically filter out spam, enhancing user experience and online security. This spam message detection system contributes to the ongoing efforts to combat spam and protect users from unwanted and potentially harmful messages, thereby improving the quality of digital communication. This paper explores the effectiveness of various machine learning classifiers in detecting unsolicited emails and texts, focusing on their accuracy and robustness. Among the classifiers evaluated, the random forest and support vector machine (SVM) classifiers demonstrated superior performance. The random forest classifier, an ensemble learning method, combines multiple decision trees to reduce overfitting and improve generalization, leveraging techniques such as bootstrapping and feature randomness. It also provides insights into feature importance, enhancing model interpretability and robustness to noise. The SVM classifier excels in high-dimensional spaces, utilizing the kernel trick to handle non-linear relationships and regularization to balance training and testing errors, making it effective in complex decision spaces. In comparison, other classifiers like K-nearest neighbors, Naive Bayes, logistic regression, and various boosting and bagging methods were less effective due to their sensitivity to data scale, feature correlations, and high-dimensionality challenges. This study highlights the advantages of random forest and SVM in spam detection, emphasizing their potential for improving email and text filtering systems. The comparative analysis revealed that random forest and SVM stood out as the most efficient algorithms for spam filtering, achieving an impressive accuracy level of 97.5%.