Over four mini projects, I explored portfolio analysis, statistical testing, derivative pricing, stochastic modeling, and machine learning. I started by building high- and low-risk portfolios using real stock data, then tested the normality of log returns using Shapiro-Wilk and other methods. I modeled option sensitivities with Black-Scholes, including delta and theta. In the final project, I implemented the Heston model with stochastic volatility, simulated delta and vega hedging, compared to GBM, and trained ML models like XGBoost to predict hedging error based on features like price and volatility.