M.S. Data Science | Python & R Developer
This profile serves as the source code repository for my applied research in behavioral analytics and algorithmic forecasting. Unlike my business portfolio, this archive focuses on the raw implementation logic, reproducibility, and statistical methodologies used in my modeling work.

• Stack: Python, Pandas, Scikit-Learn, K-Means, Logistic Regression
• Implementation: End-to-end customer intelligence pipeline integrating RFM feature engineering, unsupervised clustering, and supervised classification[cite: 1, 2].
• Logic: Applied sklearn.pipeline.Pipeline with StandardScaler to prevent data leakage, optimizing classification for 72% Recall to prioritize high-value "Whale" retention over accuracy[cite: 1, 2].
• Key Function: Automated transformation of raw transaction logs into actionable behavioral cohorts and predictive churn/spend metrics[cite: 1, 2].

• Stack: SQL (BigQuery), Looker Studio, Regex
• Implementation: Engineered a heuristic identity resolution pipeline to filter bot/developer traffic from raw GA4 event streams.
• Logic: Utilized UNNEST and Window Functions to reconstruct session-scoped engagement metrics, distinguishing "0-second" headless browsers from high-value recruitment leads (LinkedIn/GitHub).
• Key Function: Identity Logic that flags visitors based on geo-behavioral fingerprints.

• Stack: Python, Scikit-Learn, XGBoost, SHAP
• Implementation: Developed a classification pipeline using scale_pos_weight to handle class imbalance in HR data.
• Logic: Replaced standard black-box predictions with SHAP (SHapley Additive exPlanations) waterfall plots to quantify individual turnover risk factors.
• Key Function: Optimized precision-recall thresholds to improve minority class detection (attrition) from 34% to 61%.

• Stack: Python, Facebook Prophet, Pandas
• Implementation: Time-series decomposition model separating growth trends from weekly/yearly seasonality.
• Logic: Calculated dynamic "Safety Stock" levels using 95% confidence interval upper bounds rather than static averages.
• Outcome: Generated risk-adjusted procurement algorithms to minimize stockout probabilities during high-variance periods.

• Stack: R, Magick, Tmap
• Implementation: Custom "Manual Device" rendering pipeline to bypass standard library limitations on ARM64 architectures.
• Logic: Script generates individual PNG frames for urban density evolution (1950–2030) and stitches them using the Magick image processing engine.
• Feature: Hardware-agnostic rendering loop for high-resolution time-lapse generation.

• Stack: Python, SciPy, Statistical Hypothesis Testing
• Implementation: Rigorous evaluation of gameplay gate mechanics (Level 30 vs Level 40).
• Logic: Applied statistical significance testing (p-value analysis) to measure impacts on 1-Day vs 7-Day retention rates.
• Result: Quantified the negative impact of delayed friction points on long-term user habituation.

Python R SQL BigQuery Tableau Looker Studio Git

• Full Case Studies & Business Impact: View Portfolio
• Professional Network: LinkedIn Profile