Portfolio

Data Science & Machine Learning

Other

Created a Fine-tuned task specific model using Llama3 and Unsloth to create Dungeons & Dragons Statblocks.
Implented online usability through Hugging Face for Training Dataset and LoRA Fine-tuned Adaptors.

Created a Neural Network Stacking Classifier using Scikeras KerasClassifier and Scikit-learn’s StackingClassifier.
Used analysis of feature outliers and distrubtion to improve feature engineering.
Combined Catboost, XGBoost, LightGBM and Random Forest Classifier models to create a balanced ensemble model.
Utilized Tensorflow 2.0 Sequential Neural Network for improved ensemble weights.
Achieved a final score of 0.87583 (compared with best of 0.87946), placing 283 out of 1910 submisions.

Created a Convolutional Neural Network in TensorFlow to predict the class of a handwritten character.
Artificially constructured characters with a strikethrough.
Used measures to avoid overfitting a Computer Vison task, such as Dropout, Batch Normalization, Kernal Regulization, and Data Augmentation.
Achieved a 99% prediction accuracy on the test data.
Model deployed for real world application of Education Scoresheet recogniton.

Utilized Machine Learning techniques with Scikit-learn to predict character races based on ability scores and features from a Dungeons & Dragons dataset.
Explored various models for selection, Random Forest, Logistic Regression, Support Vector Classifier (SVC), K-Nearest Neighbors (KNN), and AdaBoost Classifier.
Conducted in-depth feature analysis and model evaluation.
Fine-tuned hyperparameters of the best performing classfier through manual tuning, RandomizedSearchCV and GridSearchCV.
Achieved an accuracy of 67.25% in predicting character races, improving on 51.8% from the initial dataset.

A financial assessment and report for ‘DnD with Toph’, an online Dungeons & Dragons adventure service, using Python and Pandas to clean, transform, and analyze the data.
Used data to observe growth and profit trends to optimise future scheduling and projects.
Skills include Data Manipulation & Analysis using Python & Pandas and Data Visualization with Matplotlib.

Developed a Python-based Zombie Survival Game, the mechanics involve managing resources, mission outcomes, and end-of-day round Zombie attacks.
Object-oriented programming in Python to create Class Models for Camp, Locations, and Players.
Utilized smaller functions for specific game mechanics and larger functions to implement and run game flow, mission outcomes, and end-of-round events.
Employed random library and computer decision logic trees for computer-controlled players.