Command-Line Pipeline Builder for Spam and News Classification

Before reaching for large neural models, working data scientists establish classical baselines, and the discipline of building a clean pipeline from raw text to evaluated classifier is exactly what most learners skip. Spam filtering and news categorization remain the canonical proving grounds because the tasks are intuitive while the engineering lessons are universal. The intern builds a Python command-line tool that assembles the full classical NLP workflow. Text preprocessing with NLTK handles cleaning, tokenization, and stopword removal; scikit-learn's TF-IDF vectorizer converts documents into numeric features; and training commands fit Logistic Regression, Naive Bayes, and Support Vector Machine classifiers on labeled datasets. The tool outputs proper evaluation artifacts, accuracy, precision, recall, F1, and confusion matrices on held-out data, and supports running trained pipelines on new documents to produce predictions. Comparing the three algorithms on the same data teaches how model choice interacts with feature representation. By completing the project, the intern masters the baseline methodology that professional NLP work is measured against, learns to structure a multi-stage pipeline behind a clean CLI, and demonstrates evaluation literacy, knowing not just how to train a classifier but how to prove it works.