Python ML Stack (NumPy, Pandas, scikit-learn) overview

The classical Python machine learning stack anchored by NumPy for numerical array operations, Pandas for tabular data manipulation and exploration, and scikit-learn for preprocessing, model selection, and a broad catalogue of supervised and unsupervised algorithms. This combination is the default starting point for data science and applied ML work. Matplotlib and Seaborn are typically added for visualisation. Jupyter notebooks serve as the interactive development environment. When models need to graduate to production APIs, FastAPI or Flask are layered on top. scikit-learn's consistent estimator API and Pipeline abstraction make feature engineering and model evaluation straightforward to compose and cross-validate.