• Led a multidisciplinary tech team in the development and deployment of a recommendation system for career and personality exploration using machine learning techniques.• Collaborated with the development team and different counselors and experts for career suggestion and personality exploration modules.• Contributed to strategic planning and roadmap development for the recommendation system, aligning technical initiatives with business objectives and user needs.• Contributed to ongoing improvements and iterations of the recommendation system based on user feedback and performance metrics

Objective: Prediction of Patient Mortality Rate in Hospital Built a model to predict the hospital mortality rate of admitted patients using the Random Forest with F-1score of 0.61 Analyzed the effect of the change in medical prescription on patient health and mortality rate at Beth IsraelMedical Center Used feature engineering to extract more than five important and hidden features from the raw MIMIC IIIdataset. Performed the hyperparameter tuning by GridSearchCV to optimize the built model and achieve an accuracy of77 Visualized 46,000+ data points using Matplotlib, Seaborn: established relationship between mortality andadmission type