Predictive Modeling and Machine Learning Fundamentals Questions

Applied predictive-modeling cases worked end to end on tabular business data. Covers choosing a model family for a business target (churn, lifetime value, lead scoring, conversion, support-ticket escalation), setting a baseline, defining the label, observation window and prediction horizon, catching leakage, sanity-checking and interpreting results for non-technical stakeholders (feature importance and its traps with correlated features, explaining a single flagged customer), handling censored outcomes with survival methods such as Kaplan-Meier and Cox models, turning model scores into thresholds and business actions with cost-sensitive reasoning, targeting for incremental effect with uplift modeling, and clustering and unsupervised methods in practice: k-means and its initialization (k-means++), empty clusters, scaling, distance choices and mini-batch variants, choosing and validating the number of clusters without labels (elbow, silhouette, stability), density-based, hierarchical and mixture-model alternatives, mixed numeric and categorical features, high-dimensional preparation and visualization, and customer segmentation carried through to stakeholder communication, testing whether segment-based targeting works, keeping segments stable over refits, and downstream use. Excludes regression and classification algorithm internals, metric and validation theory, feature engineering mechanics, and large-scale or production ML systems (including distributed or streaming clustering), which are covered elsewhere.