* Utilized advanced SQL queries for Data Manipulation.* Developed of robust short-term/long term Energy Forecasting Models employing advanced Machine Learning and Deep Learning methodologies. * Proficient in collecting, cleaning, and processing large datasets sourced from diverse energy sector websites to derive actionable insights facilitating data-informed decision making. * Demonstrated expertise in implementing parallel processing techniques, achieving a notable 70% reduction in process execution time. Adept at scheduling and overseeing multiple cron jobs on server infrastructure to automate data collection processes, optimizing operational efficiency. * Skilled in crafting reliable web scraping code in Python, utilizing web scraping libraries to extract pertinent data from various energy sector websites.

Projects done under GRIPJUNE21Employed Linear Regression to forecast student academic scores based on study hours with 94.2% accuracy and 4.87% mean absolute error. Visualized the relationship between study hours and academic scores using the Best Fit Line.Applied the K-Means clustering technique to identify 3 as the optimal number of clusters for the Iris dataset.Employed the Decision Tree Algorithm to predict the type of iris flower using the features provided in the iris dataset.