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Classification, clustering, and association rules are among the most fundamental techniques in data science and big data analytics. Classification is a supervised learning technique where the goal is to predict a categorical label for given input data. For example, predicting whether an email is spam or not spam, determining if a tumor is benign or malignant, classifying customer sentiment as positive or negative, predicting student performance as pass or fail, categorizing news articles into sports, politics, or technology, predicting fraud versus non-fraud transactions, assigning loan applications into high or low risk, classifying handwritten digits, identifying types of plants based on features, and categorizing animals into species based on characteristics. These examples show that classification is widely used in domains like healthcare, finance, e-commerce, and education to make accurate and automated predictions. Clustering, on the other hand, is an unsupervised learning method that groups __________ _______ _______ ____ _______ ________ __________.
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