Manu K Nath

Built and optimized scalable ETL pipelines with robust data preprocessing, transformation, and normalization using Python and SQL—boosted data accessibility by 25% and reduced errors by 10%. Designed data workflows for patent scoring and implemented Apache Spark to process large datasets, boosting efficiency by 20% and cutting project timelines by 15%. Automated data transformation and cleaning, improving operational performance and retrieval speeds by 10–15%, enabling faster data-driven decisions.

Trained and optimized machine learning models (classification and regression) for inventory forecasting, contributing to a 20% improvement in operational efficiency. Preprocessed and cleaned large datasets using Python (Pandas, NumPy), improving data quality by 20% and enabling accurate model training. Automated data extraction and visualization workflows using BeautifulSoup, Selenium, and Matplotlib, increasing training data volume by 20% and enhancing model insights.