Abstract
This study explores the detection of SMS spam utilizing TF-IDF analysis on a dataset containing a collection of text messages labeled as spam or ham (non-spam). The dataset comprises messages suitable for spam detection analysis using TF-IDF techniques. The research aims to evaluate the effectiveness of TF-IDF in distinguishing between spam and spam (non-spam) messages. The analysis involves examining the precision, recall, and F1-score metrics to assess the performance of the classification model. The results demonstrate promising outcomes, with a high accuracy rate achieved in classifying spam and ham (non-spam) messages. Additionally, the study provides insights into the distribution of spam and ham (non-spam) labels in the test data, further enhancing our understanding of SMS spam detection techniques.
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