Text Classification Using Naive Bayes: Theory & A Working Example. In this article, I explain how the Naive Bayes works and I implement a multi-class text classification problem step-by-step in Python.

**Introduction****The Naive Bayes algorithm****Dealing with text data****Working Example in Python (step-by-step guide)****Bonus: Having fun with the model****Conclusions**

**Naive Bayes** classifiers are a collection of classification algorithms based on **Bayes’ Theorem**. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other.

**Naive Bayes** classifiers have been heavily used for **text classification** and **text** **analysis** machine learning **problems**.

**Text Analysis** is a major application field for machine learning algorithms. However the raw data, a sequence of symbols (i.e. strings) cannot be fed directly to the algorithms themselves as most of them expect numerical feature vectors with a fixed size rather than the raw text documents with variable length.

In this article I explain a) how **Naive Bayes **works**, **b) how we can use **text** **data** and **fit** them into a **model** after transforming them into a more appropriate form. Finally, I **implement** a **multi-class text classification problem step-by-step in Python**.

Let’s get started !!!

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