About me

I am a data scientist with over 3 years of experience in machine learning, deep learning, data analysis, and data visualization. I hold a master's degree in Data Science from the University of Alberta. I am deeply passionate about solving complex problems and uncovering insights through the use of mathematics, data, and code.
The projects featured in this portfolio were carefully selected to showcase my skills and expertise in the field of data science. Each project was meticulously designed to address real-world problems, employing a variety of techniques such as data cleaning, data analysis, and the utilization of machine learning algorithms and deep learning models.

Pricing American Options with Q Learning

In this project, we aim to learn an optimal pricing policy Q* for American options to maximize the expected cumulative reward over time. We estimate the continuation value using Laguerre Basis functions and compare it to the return of exercising the option.

Recommender Systems: NCF and GPT

The goal of this project is to provide personalized movie recommendations based on user preferences. The project incorporates two main components: Neural Collaborative Filtering (NCF) and OpenAI's GPT.

AI Brand-Inspired Cake Generator

The AI Brand-Inspired Cake Generator combines Text-to-Image with LLM using OpenAI's GPT-3.5 Turbo with Stable Diffusion to generate visually captivating images and enticing descriptions of brand-inspired cakes.

Geospatial analysis of deprivation in Canada

In this project, I use Geopandas and Folium to visualise data on deprivation levels in major canadian cities with the help of the Canadian Index of Multiple Deprivation.

Predicting Market Value of Soccer Players

This project utilizes regression analysis to predict the market value of professional soccer players using web scraping tools and skLearn.

Particle Detection and Prediction

In this project, I collaborated with teammates to implement Convolutional and Recurrent neural networks in order to generate, detect, and finally forecast particle data movements.

Airline Passengers Satisfaction

This projects aim to compare different classification models (Logistic regression, Decision tree and SVM) to predict airline customers' satisfaction.