Extracted and analyses data from social media to predict sentiments and trends using NLP for enhancing predictive analytics capabilities. Developed and implemented machine learning models to predict the Market research and survey results including decision trees, random forests, and neural networks Performed extensive feature engineering and data wrangling on both structured and unstructured datasets to optimize model accuracy and reliability. Designed and deployed a Python (Flask)-based web application for comprehensive data analysis and visualization, integrating advanced statistical and machine learning methods. Worked on the Data pipelines for more accurate and reproducable Data analysis and to build the robust ML models to train and test the data. Expertly utilized data visualization tools such as Tableau and looker studio to create detailed intelligence reports and share ongoing insights, supporting decision-making processes with descriptive statistics and interactive dashboards. Demonstrated proficiency in SQL, Advanced Excel, and Google BigQuery for robust data extraction and analysis.

I worked as a research assistant, focusing on laser shock peening, a repair technique for aircraft engine blades and compressor blades. My research involved utilizing simulation and data Science methods, which I presented at the esteemed TMS2022 conference in California, USA

Supervised TIG and MIG welding processes, utilizing statistical quality control methods to ensure precision and adherence to specifications, demonstrating an acute ability to apply statistical techniques in practical manufacturing settings. Maintained comprehensive project records using Advanced Excel, documenting and analyzing quantities of parts manufactured to optimize production processes through data-driven decision- making. Developed and managed detailed material reports using SQL and data visualization tools like looker studio enabling efficient tracking and utilization of client-provided materials, and enhancing project