Comprehensive Customer Career Analytics & Predictive Modeling I performed a full end-to-end analysis on a 19 K-row customer career dataset: importing, cleaning, and handling missing demographics and company data; exploratory data analysis with distributions, correlation matrices, and striking bar charts; clustering learners into four segments to reveal training-vs-mobility patterns; and feature engineering (experience bins, training categories). I built and tuned Logistic Regression, Random Forest, Gradient Boosting, and a stacking ensemble to predict job-change (AUCs up to ~0.76).