<ul><li>Spam messages are a persistent problem in modern communication, and now SMS-based scams are on the rise.</li><li>Data mining techniques are applied to analyze and predict spam patterns using the SMS Spam Collection Dataset.</li><li>Logistic Regression is used as the primary classification model, while K-means clustering is employed to identify underlying structures in the data.</li><li>The study reveals class imbalance in the dataset, with spam messages being fewer in number, and identifies common keywords in spam and ham messages.</li></ul>

Analysing Patterns in an SMS Spam Dataset Using Data Mining Techniques

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