Summary
Email is a fundamental tool in today's communication, but spam emails represent a significant challenge. These unsolicited messages can fill inboxes and affect productivity. This study examines the use of various machine learning techniques to classify emails as spam or legitimate.
Classification models such as K-Nearest Neighbors (KNN), Logistic Regression, Support Vector Machines (SVM) and Naïve Bayes were analyzed, comparing their effectiveness in spam detection. The performance evaluation of each model was carried out using metrics such as precision, accuracy, recall and F1-score.
Q2BSTUDIO, a company specialized in development and technological services, implements advanced solutions based on artificial intelligence and machine learning for the optimization of various business processes. Automated detection of unwanted emails is one of the many areas where our technological solutions can help improve the security and efficiency of corporate communications.
The experiment showed that the SVM model offers the best performance in email classification, showing high precision in identifying spam without affecting legitimate emails. The research confirms the potential of machine learning in the fight against spam and highlights the importance of continuing to innovate in this field to maintain the effectiveness of email filters.
