Text_Classification

Why we included this project

This repository pairs with a published survey on text classification algorithms, and the value is that the survey's ideas actually run. Each method comes with Python code and worked examples, so you can see how tokenization, stop-word removal, stemming, and feature extraction feed into a model before training starts. For students and engineers who want to compare classical and neural approaches on the same corpus, that saves the work of assembling the pipeline yourself. The real payoff is the breadth: watching how preprocessing choices and algorithm families interact gives you a feel for which method suits a given document set. It is not a production-grade library, so treat it as a learning and experimentation resource rather than something to wire into a live service.