• Extracted and cleaned sensitive data from multiple sources using Python, merging datasets to facilitate comprehensive analysis. This reduced data analysis time by 25% and enhanced team accessibility by storing data in CSV files.• Conducted in-depth data analysis and organized datasets using Python and Microsoft SQL, leading to a 30% improvement in load times and enhancing overall efficiency across the team projects.• Length of Stay Prediction for Heart Implant Patients: Developed a tree-based model achieving an accuracy score of 79.8% to predict LOS, which provided insights to optimize resource allocation and reduce hospital costs based on predictive outcomes.• Analyzed parameters to determine whether hernia patients should be treated as inpatients or outpatients, as identification of outpatient care can reduce cost of diagnosis by 2 to 3 times.

Data Analysis using SAP Analytics Cloud• Customized SAC analytics app UIUX with JavaScript, improving user interaction and enhancing overall usability by 25%.• Developed impactful data stories and dashboards in SAP SAC, facilitating clear communication and analysis of key business metrics, which increased stakeholder engagement by 30%.• Built effective planning models in SAP SAC, driving informed data based decision-making and optimizing resource allocation, leading to a 15% improvement in forecasting accuracyRAG System implementing using LLM-• Architected and developed a Retrieval Augmented Generation (RAG) system using Python, Langchain, and Streamlit, enhancing user engagement by 35% through the integration of external data sources facilitating user interactions via a web interface.• Executed the development of a custom AI chatbot using OpenAI's GPT-4 that improved first contact resolution rates by 30%, enabling a more efficient customer service workflow with reduced follow-up inquiries.

Data Analysis (Apr 23-Oct 23)• Expanded data collection methods to ensure comprehensive and reliable data, resulting in improvement in data accuracy.• Utilized advanced SQL techniques to identify and resolve potential errors, leading to more accurate data analysis and decision-making.• Investigated data anomalies, successfully contributing to a 20% reduction in data-related issues and warnings and created Tableau Dashboard for the same.Business Analysis & Quality Testing (Jan 23-May 23)• Conducted thorough reviews of requirements and technical specifications, leading to a 25% reduction in post-implementation defects.• Leveraged Oracle SQL for robust back-end data validation, ensuring data integrity and adherence to business requirements.• Streamlined communication with developers, improving issue resolution time and promoting efficient collaboration. Data Visualization and Reporting using Solidatus Tool (Jan 22-Jan 23)• Automated 5 primary tasks of Solidatus Tool using Python, receiving recognition from client management for increasing productivity by 40%.• Created a Solidatus model enabling data tracing from reliable sources, enhancing reporting accuracy and, supporting audit processes.• Successfully integrated new information into the model, accommodating changes in the structure and lineage for seamless data tracking.

Automation testing of Tableau using UiPath (Aug 21-Dec 21)• Developed UiPath automation for Tableau testing, reducing manual efforts by 70% and ensuring comprehensive testing coverage.• Implemented automation to validate drop-down fields, ensuring dashboard data consistency and accuracy.• Streamlined reporting processes, enabling the generation of PDF, Excel, Image, PowerPoint, and workbook outputs, resulting in a 50% reduction in reporting time.

• Developed a machine learning model using Python, pandas, scikit-learn, and time series analysis to predict demand for hardware machinery, optimizing supply chain operations and improving inventory management efficiency.• Achieved a 20% reduction in prediction error by analyzing historical sales data with the developed model, enhancing supply chain assistance and operational efficiency