Supervised Learning Model for the Automatic Identification of Spam in Mobile Messaging Environments
DOI:
https://doi.org/10.61799/2216-0388.1797Keywords:
classification, machine learning, mobile messaging, Random Forest, supervised model, spam, text processingAbstract
This article presents a supervised learning model for the automatic detection of unwanted messages (spam) in mobile messaging environments, using data obtained from the Hugging Face repository. The methodology included text cleaning and normalization, translation into Spanish, exploratory analysis, and message representation through the bag-of-words technique (CountVectorizer). The dataset was then split into training and testing sets, and a Random Forest classifier was trained using manually tuned hyperparameters. The results show an overall accuracy of 96%, a precision of 1.00 for the spam class, and a significant improvement in its F1-score (0.82). Additionally, five-fold cross-validation reported a mean accuracy of 94.4%, demonstrating the model’s stability. Overall, the proposed approach proves to be an effective alternative for spam detection in mobile messaging applications.
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