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To build the classifier, we followed a systematic approach that involved several key steps. First, we loaded the dataset, which typically consists of a collection of messages labeled as spam or ham. This dataset serves as the foundation for training and evaluating our classifier. Next, we performed data preprocessing and feature extraction. This involved transforming the raw text messages into numerical feature representations that ML algorithms can process.