Projects
Academic and personal projects showcasing my data science skills.
Predictive Pitstop Strategy in Formula 1 Using Machine Learning
Predicted optimal Formula 1 pitstop windows and positional outcomes by building ML models in Python (VS Code) using FastF1, pandas, and scikit-learn; trained Random Forest, Logistic Regression, and SVM models and achieved 0.91 validation R² for pit-window prediction.
SEC Financial Data Analysis with Neo4j
Analyzed SEC company relationships by building a graph-based financial analysis pipeline using Python (VS Code), Neo4j, and Cypher on 10-K/10-Q data; identified revenue/debt clusters and sector linkages to generate actionable business insights faster than tabular-only analysis.
NYC Graduation Rates Analysis
Evaluated NYC graduation disparities by performing EDA and statistical trend analysis in Python (VS Code) with pandas, NumPy, matplotlib, and seaborn; uncovered cohort, district, gender, and ethnicity-based patterns and produced equity-focused recommendations.
Airline Passenger Satisfaction Analysis and Prediction
Predicted airline passenger satisfaction by developing end-to-end ML workflows in Python (VS Code) using pandas, scikit-learn, and LightGBM; identified key drivers such as inflight WiFi, online boarding, and class type, enabling targeted service-improvement recommendations.
Minnesota Metro Interstate Traffic Volume Prediction
Forecasted Minnesota metro interstate traffic volume by training LightGBM models in Python (VS Code) with engineered temporal features from pandas; achieved high R² with low RMSE and delivered practical insights for congestion planning and traffic operations.