EDA + Predictive Model

A complete, narrative-driven data science project taking a raw dataset from first look to a trained model — emphasizing storytelling, visual analysis, and interpretable results alongside technical rigor.

Data science projects often fail not from bad models but from poor problem framing, weak feature engineering, or uninterpretable outputs. The challenge is to build something rigorous that a non-technical stakeholder can also understand and trust.

Started with a thorough exploratory phase — distributions, correlations, outlier analysis, and class imbalance checks. Engineered domain-relevant features before modeling. Trained and compared multiple classifiers with cross-validated hyperparameter search. Produced a full evaluation report with confusion matrix, ROC curves, and SHAP-based feature importance.

A reproducible, well-documented data science notebook demonstrating the full ML workflow. A model with strong validation metrics and interpretable output — the kind of deliverable that builds client trust on Upwork and in corporate settings alike.