Building automated data cleaning pipelines, structural ETL frameworks, and interactive business intelligence systems to convert raw organizational assets into measurable economic leverage.
| Project | Core Stack | Business Impact & Operational Discovery |
|---|---|---|
| Retail Revenue Intelligence | Power BI SQL Power Query ETL |
Engineered an automated transformation pipeline for a 30,000+ transaction matrix. Identified an unaddressed risk where a single product category drove 83% of total revenue, presenting supplier-diversification protocols to safeguard operational margins. |
| Predictive Customer Churn Ecosystem | R randomForest arules ggplot2 |
Constructed a behavioral RFM segmentation model evaluating 2,000+ active client accounts. Deployed a tuned Random Forest classification model optimized via ROC/AUC to map churn patterns and protect customer lifetime value (LTV). |
| Brand Sentiment Intelligence Engine | R tidytext NLP Sentiment Analysis |
Formulated an end-to-end NLP data pipeline leveraging custom tokenization and Bing/NRC lexicons. Structured unindexed text metrics into quantitative reputation dashboards for dynamic market trend tracking. |
========================================================================================
[Core Languages] :: Python | R | SQL (MySQL, PostgreSQL)
[Data Engineering] :: ETL Pipelines | Data Normalization | Power Query | Pandas | NumPy
[Business Analytics] :: RFM Segmentation | Predictive Modeling | Market Basket Analysis
[Systems & DevOps] :: Git Version Control | Data Governance | Advanced Excel
========================================================================================