Build a Text Classification Pipeline with Python and NLP

Text classification is one of the foundational tasks in natural language processing (NLP), powering applications from email spam filters and sentiment analysis engines to content moderation systems and customer support routing. At its core, text classification transforms unstructured human language into structured predictions by converting raw text into numerical feature vectors and training a machine learning model to assign labels based on patterns in those features. The classical NLP pipeline - tokenization, stopword removal, stemming or lemmatization, TF-IDF vectorization, and supervised classification - remains a practical and interpretable approach widely used in production systems alongside modern deep learning methods.

In this lab, you will build a complete text classification pipeline using Python and scikit-learn. Starting with the SMS Spam Collection dataset, you will preprocess raw text messages through tokenization and lemmatization, convert them into TF-IDF feature vectors, and train both a Naive Bayes and a Support Vector Machine (SVM) classifier to distinguish spam from legitimate messages. You will evaluate each model using precision, recall, F1-score, and confusion matrices, then assemble the entire workflow into a reusable scikit-learn Pipeline object that processes raw text end-to-end.

Objectives

Upon completion of this intermediate level lab, you will be able to:

• Preprocess raw text data by tokenizing, removing stopwords, and lemmatizing with NLTK
• Vectorize text documents into numerical features using TF-IDF with scikit-learn
• Train a Multinomial Naive Bayes classifier for text classification
• Train a Linear SVM classifier and compare its performance against Naive Bayes
• Evaluate classification models using precision, recall, F1-score, and confusion matrices
• Build a reusable scikit-learn Pipeline that chains preprocessing, vectorization, and classification

Who is this lab for?

This lab is designed for:

• Data science and machine learning students building classical NLP pipelines
• Python developers learning text preprocessing and feature engineering techniques
• Anyone preparing for roles involving natural language processing

Familiarity with Python, pandas DataFrames, and basic machine learning concepts (train/test splits, supervised classification) is recommended.