Pranav Rajaram
Data Science, UC San Diego
"Creative data scientist with strong fundamentals in machine learning, software engineering, and AI development. Shows high upside in predictive modeling and full-stack development environments, with a knack for data visualization and clear communication."
Game Film
Highlight reel of key projects showcasing technical skills and problem-solving abilities.
Developed a machine learning pipeline on MiLB tracking data to model baserunning with logistic regression and optimize cutoff play decisions with a Random Forest classifier, visualized through an interactive dashboard.
Utilized XGBoost to model NFL tracking data, assigning tackle probabilities to plays to develop a new 'Tackle Rate over Expectation' metric. Commended for quality of visualizations and animations by NFL analytics departments.
Used Aerial and Terrestrial Laser Scanning data to build 3-D Canopy Height Models, generate engineered treelists, and train machine learning models to predict tree species and forest attributes for wildfire simulation.
Built a Random Forest Regression model with scikit-learn to analyze a dataset of power outages, used techniques like one hot encoding, hyperparameter tuning, and cross-validation to optimize model predictions.