B.Tech Civil Engineering, Delhi Technological University | B.A. (Hons.) Psychology, IGNOU
Data analytics and data science portfolio, focused on statistical rigor and business-translatable insight.
Patient Journey Analytics & Treatment Persistence Prediction
Predicting Type 2 Diabetes medication discontinuation using synthetic patient data. XGBoost with temporal validation (AUC 0.70, top-decile capture 68%), Kaplan-Meier and Cox proportional hazards survival analysis, SHAP explainability, and patient risk segmentation.
Python PySpark SQL XGBoost SHAP Power BI
A/B Testing Analysis
Designed and evaluated an A/B test on an Express Checkout feature across 20,000 users. Measured a 12.7% conversion lift at p = 0.0006, validated for sample-ratio mismatch and novelty effect before issuing a go/no-go recommendation.
Python Statistics SQL
Product Funnel Analysis
Mapped stage-wise conversion across 27,209 sessions, isolated the largest drop-off point, and sized the revenue opportunity from closing the gap.
SQL Python Power BI
Machine Learning & Statistics: Logistic regression, random forest, XGBoost, clustering, survival analysis, hypothesis testing, A/B testing and experiment design, feature engineering, model evaluation, SHAP
NLP & Unstructured Data: Text preprocessing, TF-IDF, topic modeling, named entity recognition, transformer fine-tuning
Programming & Data: Python (pandas, NumPy, scikit-learn), SQL (MySQL, PostgreSQL), PySpark, Git
Business Analytics: Hypothesis-driven problem solving, market sizing, funnel analysis, RFM segmentation, cohort and retention analysis, KPI design
Visualisation & Reporting: Power BI, Tableau, Advanced Excel