Naive Bayes Classification (Part 2): How to Classify Documents?(Practical Data Analysis 15)

Learn Naive Bayes for text classification, sentiment analysis, spam detection, and TF-IDF calculation using sklearn's powerful tools and techniques.

Welcome to the "Practical Data Analysis" Series

Table of Contents

Last time, we discussed the working principles of Naive Bayes. Today, let's explore how these principles guide practical applications.

Naive Bayes Classification (Part 1): How to Teach Machines to Differentiate Genders?(Practical Data Analysis 14)

Naive Bayes Classification in Real-World Scenarios

Naive Bayes is best suited for tasks like text classification, sentiment analysis, and spam detection. Both sentiment analysis and spam detection involve judgments based on textual data.

You can observe that these three scenarios fundamentally boil down to text classification, an area where Naive Bayes excels.

Consequently, Naive Bayes is often used in natural language processing (NLP) tools.

Today, I’ll guide you through a document classification project using Naive Bayes, leveraging the powerful machine learning library sklearn.