scikit▌

Classical machine learning with scikit-learn for classification, regression, clustering, and preprocessing. \n \n Covers supervised learning (linear models, trees, SVMs, ensembles, neural networks), unsupervised learning (K-Means, DBSCAN, PCA, t-SNE), and model evaluation with cross-validation and hyperparameter tuning \n Includes preprocessing transformers for scaling, encoding categorical variables, imputing missing values, and feature engineering \n Provides Pipeline and ColumnTransformer for

scikit-bio is a comprehensive Python library for working with biological data. Apply this skill for bioinformatics analyses spanning sequence manipulation, alignment, phylogenetics, microbial ecology, and multivariate statistics.

scikit-survival is a Python library for survival analysis built on top of scikit-learn. It provides specialized tools for time-to-event analysis, handling the unique challenge of censored data where some observations are only partially known.