I have always been passionate about data, the way it can uncover patterns, answer tough questions, and drive better decisions. Whether it is crunching numbers or building tools, I enjoy using data to create clarity out of complexity.

I am also a huge sports fan. I support Manchester United in football and also a long-time LeBron James fan in basketball. Naturally, my love for sports often crosses over into my work, which is why many of my data projects are built around sports stats and analytics.

My goal is to keep blending my interests in data, sports, and automation to build things that are insightful, useful, and a little bit fun.

In my free time, I enjoy diving into data-driven projects that blend analytics, prediction, and storytelling. Whether it is analyzing MVP trends, debating the GOAT in basketball, or finding patterns in lottery numbers, I love using data to explore ideas and uncover insights.

• NBA MVP Prediction: Predicting MVP outcomes using machine learning and season stats.
• LeBron vs Jordan Analysis: Deep-dive player comparison based on data and era-adjusted metrics.
• Toto Dashboard: Interactive number analysis and prediction tool.

As a basketball fan and data enthusiast, I was curious whether we could predict MVP shares using players and teams season stats. This curiosity led me to build a machine learning model that forecasts MVP voting outcomes based on historical data.

I experimented with an ensemble model (Linear Regression, Gradient Boosting, XGBoost). Throughout the project, I focused on model interpretability, cross-validation, and feature importance to understand which metrics truly influence MVP results.

The LeBron vs Jordan debate has always been filled with strong opinions, so I wanted to cut through the noise and approach the question from a more analytical angle. Using a blend of statistics, historical context, and weighted metrics, I compared the two players across career longevity, regular season dominance, playoff success, and awards.

I built this as a structured, data-first deep dive to spotlight patterns fans often overlook, like how scoring trends changed by era. Beyond just the stats, the project pushed me to think critically about framing narratives with data and storytelling.

This project combines data analytics and machine learning to help users make more informed Toto predictions. The dashboard offers interactive tools to explore past draw trends and run predictions using a model trained on historical results.

Users can dive into number patterns, frequency stats, and model-generated forecasts all in one place. Built with usability in mind, this tool is a practical example of applying data science to real-world games of chance.