This repository explores the realm of Natural Language Processing (NLP) through an in-depth analysis of text classification models. Through the implementation of diverse data preprocessing techniques and feature encoding methods such as TF-IDF and embeddings, the project investigates the performance and efficacy of various NLP models. The primary objective is to evaluate model effectiveness using the Macro-averaged F1 score, providing insights into the comparative strengths of different approaches in text classification.