Ethan Shapiro’s Portfolio

League of Legends Champion Recommendation Engine

Project overview: As someone who has consistently struggled to make it past Diamond despite playing League of Legends for multiple years, I think it’s time to see if I can get a leg up on the enemy team. One of the major ways to easily lose the game before it starts is to draft a bad team composition. If you’re unfamiliar with League of Legends, it’s essentially a game where two teams of 5 face off against each other with the objective of breaking into the other teams home and destroying it. I might have made it sounds worse than it actually is… In any case, before the game actually starts each team gets to choose 5 characters out of over 160 and ban (not allow) the enemy team to choose 5. In total, that’s 10 chosen champions with 10 banned champions, leaving over 150 champions to choose from (that’s over 590 million different combinations of teams!) With so many choices, it’s hard to always pick the right 5 in order to beat the enemy. This is where my model comes in.

Based off of previous 2022 paper called DraftRec, I’ve rewritten the model updating all previous code as well as collected new data taking almost 6 months of time and over 50GB of space.

The outcome will be a new recommender system that takes into account enemy team’s skill in addition to what it already accounts for. At the current time I’m training the DraftRec model to get baselines and will be enhancing the model after.

Technical Knowledge: Transformer Networks, Neural Networks, Data Cleaning, Feature Engineering

Tools: Python, PyTorch, AWS EC2

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Steam Review Sentiment Analysis (Big Data)

Project overview: In this project, I worked with a 35GB Steam reviews dataset to sharpen my big data and NLP skills using PySpark. The primary focus was on practicing essential PySpark concepts like partitioning, groupbys, joins, and efficient computation techniques to handle large-scale data. I aimed to optimize calculations and improve processing speed, gaining valuable experience in managing big datasets.

I also explored natural language processing using PySpark NLP and John Snow Labs’ NLP library. This involved cleaning the review data, performing entity recognition using pre-trained models, and conducting unsupervised sentiment analysis using TextBlob and clustering. These tasks helped me practice working with complex text data and extracting meaningful insights from unstructured reviews.

One of the main challenges I encountered was setting up PySpark locally due to compatibility issues with various package versions. After overcoming these versioning hurdles, I successfully ran the analysis and achieved my goal of applying both big data techniques and NLP in a real-world context.

Technical Knowledge: Big Data Processing, Natural Language Processing (NLP), Entity Recognition, Sentiment Analysis

Tools: Python, PySpark, PySpark NLP, John Snow Labs NLP, Steam Reviews Dataset (40GB)

Soft Decision Trees Research Project 🌳

Project overview: In our final-year project, my team of two classmates and I (supervised by two professors) explored how soft decision trees (SDTs) can improve machine learning model interpretability while maintaining high performance. Unlike traditional decision trees, which make binary splits, SDTs use probabilistic decisions to handle ambiguity more effectively. Our research focused on applying feature learning techniques and leveraging the Neural Feature Matrix (NFM) to visualize and interpret the features learned by the model.

We tested our approach using datasets like MNIST and CelebA, demonstrating that SDTs could not only match the accuracy of neural networks but also offer insights into the decision-making process. On MNIST, we successfully replicated prior work, showing that SDTs could accurately classify digits and explain the decision paths. For the more complex CelebA dataset, the model identified key facial features, further validating SDTs’ ability to handle real-world data. Finally, we combined our previous neural network with features learned from the SDTs and found the neural network improved its accuracy within the same amount of training epochs by 8%.

Our findings highlight the potential of SDTs to combine the flexibility of neural networks with the transparency of decision trees, improving overall accuracy in both regards. By making decision-making processes clearer, SDTs are promising for applications requiring interpretable AI, such as medical diagnoses or financial models.

Technical Knowledge: Matrix Calculus, Neural Networks, Soft Decision Trees, Feature Learning, Data Visualization, Neural Feature Matrix (NFM)

Tools: Python, PyTorch, MNIST, CelebA

Fantasy League of Legends

Project overview: In my Fantasy League of Legends project, I aimed to integrate various skills learned in class and through personal study. I built a full-stack application where I used Go to develop a CRUD backend that facilitates API requests between the frontend and a prediction model. The frontend, designed with Next.JS, was deployed on Firebase as an interactive single-page application for users to select players and predict game outcomes.

For the prediction model, I collected data from the RIOT API and trained a PyTorch model in Python. I then deployed this model to an AWS Lambda API endpoint to predict match winners based on the selected players. To ensure reliability, I wrote test cases using Cypress for the frontend, Testify for the backend, and PyTest for the Lambda API endpoint. This project brought together everything from backend and frontend development to machine learning deployment, offering a full end-to-end experience.

Technical Knowledge: Data Collection (RIOT API), Machine Learning, API Development, Frontend/Backend Development

Tools: Python, PyTorch, AWS Lambda, Firebase, Next.JS, Go, Cypress, PyTest, Google Cloud Run

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