Project I have worked on aims to develop a predictive model for SQL query completion using deep learning, machine learning, and data mining techniques. The model leverages NLP and sequence-to-sequence models to parse SQL queries and predict subsequent parts, aiming to enhance prediction accuracy. Additionally, the project involves designing an ETL pipeline to preprocess and segment large datasets, focusing on improving model training efficiency and reducing prediction latency, ultimately contributing to more intuitive and efficient database management.

Worked with huge data on imports and exports of pharmaceuticals products. Logistical efficiency was achieved with the predictive modeling techniques.