Develop Naïve-Bayes Classifier from Scratch

The naive bayes algorithm is a fast and interpretable bayes classification algorithm widely used for probabilistic prediction. In simple terms, a bayes classifier estimates the probability of each class given the input features using a bayesian classification algorithm, then selects the class with the highest posterior probability. This project helps us understand what is naive bayes by walking through naive bayes classification step by step, grounded in Bayes’ rule and the “naive” independence assumption.

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We will implement a Naive Bayes classifier in Python, instead of relying on high-level libraries. By writing the probability computations by ourself in this hands-on project, we’ll see exactly how the Naive Bayes classifier formula processes features and how a Bayesian classifier converts conditional probabilities into final predictions.

We’ll apply our implementation to the US Census dataset, applying the data preprocessing steps essential for any Naive Bayes machine learning workflow. By coding the logic manually, we’ll move beyond a simple Naive Bayes example to internalize the mechanics of feature preparation and label handling.

Finally, we’ll evaluate our model against Scikit-learn to establish an objective performance benchmark. This comparison bridges the gap between our custom implementation and industry-standard tools, while providing a conceptual link to the Bayesian optimal classifier theory.