Multi-Dataset Analytics Core
The Architecture
Automated feature generation models designed to analyze massive real-world customer tables, implementing algorithmic corrections that lifted baseline classification performance by 12%.
This core was engineered during my ML Internship to clean, merge, and visualize complex datasets utilizing cutting-edge Python libraries, forming the foundation of a predictive machine learning pipeline.
Data Visualization
Translating thousands of rows of relational data into high-contrast Matplotlib visualizations to instantly identify correlation and outliers.
ETL Pipelines
Optimized Extract, Transform, Load (ETL) pipelines using Pandas and SQL queries to sanitize raw data before ML ingestion.
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