Tejaswi Yellanki

Master's student in Health Data Science (graduating August 2025) with hands-on experience in clinical research, data analytics, and machine learning. Proficient in Python, SQL, and BigQuery, with a strong foundation in statistical analysis and predictive modeling. Passionate about applying data-driven insights to improve healthcare outcomes and optimize clinical decision-making. I am actively seeking a full-time position in data analysis or healthcare analytics, where I can contribute to meaningful healthcare innovations.

• Model United Nations Leadership, Organizer and Delegate at Confluence Model United Nations (2019, 2020)
• Academic Recognition, 1st Place, e-Poster Presentation, RBVRR Women's College of Pharmacy
• Community Service, Active Volunteer with National Service Scheme (NSS), leading healthcare awareness programs in local communities

• Mental Health Trends Analysis & Prediction: Analyzed global mental health datasets to build predictive models for workplace mental health outcomes. Achieved 84.8% accuracy using ensemble methods and advanced feature engineering. Tools: Python, R, Random Forest, Dask, profvis
• Comprehensive Breast Cancer Analysis & Prediction : Developed and optimized machine learning models to predict breast cancer outcomes using 2019 patient data. Implemented multiple algorithms and performed comprehensive model evaluation. Tools: Python, Scikit-learn, Logistic Regression, SVM, Random Forest
• Diabetes Health Outcomes Analysis : Merged and analyzed large-scale datasets from CDC/Kaggle using SQL in BigQuery. Identified significant regional and lifestyle-based trends in diabetes prevalence across demographics. Tools: SQL, BigQuery, Python, Statistical Analysis
• Health Data Analysis Project : Explored complex relationships between obesity, physical activity, and chronic diseases using advanced statistical methods and data visualization techniques. Tools: Python, Pandas, Matplotlib, Seaborn