ML Cohort 8 @ U of T DSI · Python · R · SQL
I build pipelines — data, community, and curriculum.
Finishing ML program at U of T DSI. Pushing R, SQL & Guelph portfolio to GitHub soon.
Supporter of women & gender diverse people in tech (Science Riot Grrls, McMaster University, Seneca Polytechnique).
5 classifiers · 20,316 incidents · XGBoost AUC 0.770
My contributions:*
- Data cleaning pipeline — missing values, delay code standardization, outlier handling
- 16+ presentation-ready visualizations using TTC brand colours
- Station analysis: severity vs. frequency — two different operational problems
- Incident type breakdown across 5 categories (Security, Equipment, Operations/Medical, Plant/Infrastructure, Transportation)
View project → · Video reflection →
Python R R Shiny SQL Hadoop XGBoost LightGBM scikit-learn
TensorFlow PyTorch pandas NumPy Seaborn Git Salesforce Airtable
| Machine Learning Software Foundations | U of T DSI · 2026 |
| Data Science Certificate (Distinction) | U of Guelph · 2024 |
| Neural Networks & Deep Learning | DeepLearning.AI · 2024 |
| Python Essentials | Cisco · 2021 |
| Technical & Professional Communication | York University |
| B.Sc. Psychology | York University |
| Luminance HDR tutorial | 13k views → |
| Makerspace curriculum mockup | YouTube → |
| Adobe Captivate e-learning | Available on request |
LinkedIn · Kaggle · Open to data roles where the work matters. Open to data roles where the work matters.

