Aditya Gunda

Football Player Market Value Prediction

• Cleaned and pre-processed 50,000+ FIFA player records using Python (Pandas, NumPy, Scikit-learn), reducing data inconsistencies by 35% • Applied PCA for dimensionality reduction and evaluated multiple ML models (XGBoost, Random Forest, Regression), improving prediction accuracy by 20% and reducing MAE by 15% • Conducted comprehensive EDA and feature engineering to identify key value drivers, achieving Grade A- performance

Medicare Hospital Finder & Analytics Dashboard

• Analyzed Medicare Inpatient Hospital dataset containing hospital discharge records across multiple US states and diagnostic groups, processing data for 1000+ hospitals • Built interactive Tableau dashboards featuring geographic maps, choropleth visualizations, and tree maps to compare hospital performance metrics including total discharges, Medicare payments, and covered charges • Designed data-driven insights for Federal Health Insurance stakeholders to optimize Medicare program by analyzing discharge patterns, payment trends, and geographical distribution of medical services

Big Mart Sales Prediction

• Processed and cleaned 8,523 retail sales records across 1,559 store-item combinations using Pandas and NumPy • Built and evaluated Linear Regression and Random Forest models achieving an R² score of 0.57 and an RMSE of 1,079 with 5-fold cross-validation