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Uses a Multinomial Naïve Bayes model to predict passengers’ embarkation port from key demographic and fare features.
<div>The project loads the Titanic CSV, fills missing values (Age, Fare, Embarked) using medians to avoid warnings, and label-encodes categorical data. It selects features (Pclass, Sex, Age, SibSp, Parch, Fare) with the target set to “Embarked.” After splitting into train/test sets, it trains a MultinomialNB classifier and evaluates performance via accuracy, confusion matrix, and classification report .</div><div><br></div>
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