Currently, I'm a third-year student studying Data Science at Simon Fraser University (SFU). I am also the current President of the Data Science Student Society (DSSS) and the acting Director of Educational Events in the Computer Science Student Society (CSSS).
I really enjoy learning about new tools to do data analysis within varying domains. My goal is to continue to grow and learn every day, developing my skills and data-driven intuition.
I'm seeking for my first co-op opportunity to apply and validate the skills I've practiced through my personal projects.
Languages: Python, R, SQL
Tools: PowerBI, SMSS, SQL, Streamlit, Excel, Git, GitHub, Docker, Jupyter Notebooks
Currently, I'm learning more about PowerBI and integrating SQL to create clean and efficient visualizations.
I've recently worked on a few projects that I've been able to learn and apply new skills.
Telco Customer Churn Predictor 🔗
I used Python within Jupyter Notebooks to perform EDA, pre-processing, and initial modelling to model customer churn from IBM's Telco dataset. After, I modularized each step using Python files, trained and tuned 3 different ML models (XGBoost, LightGBM, Random Forest) using MLFlow and Optuna to observe and preserve each model artifact for full reproducability.
Using the optimally trained model, I deployed a user-facing Streamlit dashboard, which predicts a specific customer's churn probability based on their current internet plan, and identifies the most significant singular changes to reduce the customer's churn probability.
PlantCo Sales Dashboard 🔗
Using PowerBI, I created an interactive reporting dashboard that is able to compare YTD vs PYTD values on 3 metrics - Quantity, Gross Profit, and Income.
This dashboard uses switches to dynamically change between each year, ranging from 2022 - 2024, and to change between each metric bsaed on the stakeholder's interests. The high level visualizations allow for drilling down, so stakeholders can easily investigate both positive and negative trends in the data.
Bike Shop KPI Dashboard 🔗
Another PowerBI project; however, this time I used SSMS (SQL Server Management Studio) and SQL to query the data into PowerBI through a live connection. The dashboard features a simple year splicer, a few easy-to-read visualizations, and a drill-down into the busiest hours for each day of the week.
For any inquires, feel free to contact me via email, or through my Instagram, and I will get back to you ASAP!