You will analyze business requirements and translate them into appropriate machine learning task types, ensuring correct problem framing before modeling begins.

You will use ML APIs to train and compare multiple algorithms on structured datasets using reproducible workflows.

You will evaluate model behavior across algorithm families and justify selection decisions using bias–variance reasoning and performance evidence.

You will examine default hyperparameters and computational complexity to understand how they influence model behavior and training cost.

You will design structured search strategies, run tuning experiments, and interpret cross-validated results to improve model performance.

You will benchmark training time, memory usage, and computational cost to select algorithms that meet performance and efficiency goals.

You will implement k-fold and stratified validation strategies to generate reliable performance estimates, especially for imbalanced datasets.

You will interpret feature-importance outputs and SHAP explanations to clearly communicate model behavior to technical and non-technical stakeholders.

You will construct, tune, and package an automated machine learning pipeline that integrates preprocessing, model training, and optimization into a reusable workflow.