In this video, we dive into the Naive Bayes algorithm, a powerful supervised machine learning model widely used for tasks such as spam detection and fraud identification. We'll start by explaining the fundamental concepts behind Naive Bayes with a simple example, showing how it classifies messages as spam or real. Then, we move on to a hands-on project where we build and train a Naive Bayes model using a dataset of messages. We’ll guide you through the steps of data preprocessing, including handling missing values and ensuring balanced classes, followed by tokenizing the text data. We'll demonstrate how to apply the model, analyze its performance using confusion matrices, and evaluate key metrics like precision and recall. Finally, we'll test the model with new data to see its practical application in real-world scenarios.
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