• Currently working on technical advancements for MTC BPDC.

𝐏𝐫𝐨𝐣𝐞𝐜𝐭: • Led a steel plant project, creating a data pipeline that saved 20 days per month. • Executed a machine learning project to optimize CO2 emissions, aligning with environmental goals. • Developed an app for real-time CO2 forecasting using an optimized Meta Prophet model. • Integrated a PowerBI dashboard for detailed insights. • Deployed an LLM-based application on AWS to simplify complex visualizations into effective insights using natural language processing, driving digital transformation in data analysis. • Enabled engineers to access actionable insights, improving decision-making in plant operations and emission analysis.𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 & 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: • Researched iron processing plants to pinpoint key CO2 emission factors. • Applied feature engineering to optimize parameters, reducing emissions.𝐌𝐢𝐥𝐞𝐬𝐭𝐨𝐧𝐞𝐬: • Completed RPA training and built practical bots with Automation Anywhere. • Demonstrated collaboration skills, strong work ethic, and deep understanding of manufacturing processes.

𝐓𝐞𝐜𝐡 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 & 𝐒𝐤𝐢𝐥𝐥𝐬:• Completed an extensive training program covering airline industry fundamentals and revenue optimization strategies.• Led the development and execution of the Groups Materialization Prediction, an ML-based project integral to OR team applications.• Explored and experimented with several machine learning and deep learning models.• Conducted thorough data cleaning and applied various feature encoding techniques to improve model accuracy.• Skillfully implemented the XGBoost regressor, achieving significant improvements in model performance through meticulous hyperparameter tuning.• Utilized libraries like Plotly, Folium, and Polars to create dynamic and informative data visualizations.• Developed fully functional applications using Streamlit and Dash.• Gained experience in using Snowflake Data Cloud.• Employed innovative feature engineering skills for feature selection and improving model accuracy.𝐀𝐜𝐡𝐢𝐞𝐯𝐞𝐦𝐞𝐧𝐭𝐬:• Successfully presented the application to the Divisional VP, Revenue Optimization and the Operations Research team, earning positive feedback and commendation.• Recognized for introducing and delivering a presentation on PyGWalker, a data visualization library.• Developed an interactive application for real-time predictions and data visualizations in group bookings analysis.• Enhanced communication and presentation skills through regular meetings and project updates.

𝐌𝐢𝐥𝐞𝐬𝐭𝐨𝐧𝐞𝐬:• Acquired insights into the Finance and Microlending sectors.• Enhanced skills in Machine Learning model development.• Gained experience in a professional setting, learning the operations of a FinTech company.• Focused on developing a Logistic Regression model using pandas and statsmodels.