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Data & Machine Learning Projects

Advanced Yu-Gi-Oh Calculator

  • Competition

  • Statistics

  • Linear Algebra

  • Machine Learning

Yu-GiTools is an advanced custom Yu-Gi-Oh calculator programmed in C++. YuGi-Tools uses a set of algorithms and mathematical models that I created using statistics, linear algebra, and multivariable calculus. Additionally, Yu-GiTools uses a relational database (SQLite) to store and manage Yu-Gi-Oh card data, streamlining the deck configuration process. Yu-GiTools was designed to give me a competitive edge in international Yu-Gi-Oh competitions by leveraging applied statistics, incorperating features such as: tournament success prediction, deck configuration optimization (using gradient descent), opening hand prediction, and more. Direct application of Yu-GiTools increased my tournament success rate by 37%.

Derivation and creation of mathematical models

Sample calculation

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green and red light wallpaper

Image Compressor

  • Unsupervised Learning

  • Clustering Algorithms

  • Machine Learning

Implemented the k-means clustering algorithm to create an image compressor with Python

Before and After Result

Roller Coaster Ranking

  • Collaborative Filtering

  • Data Cleansing

  • Data Management

Before vs After Imputation

Data Migration: Created Data Pipelines with Python and pandas to migrate over 300 roller coaster rankings to a SQLite database from various Excel spreadsheets


Machine Learning & Data Cleansing: Implemented collaborative filtering using TensorFlow to impute missing data-points with 92% accuracy

Sample Code in Python

Located 10 previously overlooked roller coasters with world-class potential through insights gained from the improved dataset