In this project, my goal was to predict the cost of starting a business in different countries around the world. I decided to use the World Development Indicators dataset, which presents "the most current and accurate global development data available, and includes national, regional, and global estimates."
This was my first national Kaggle competition. It is hosted by CANSSI (Canadian Statistical Sciences Institute) and the goal was to analyze and predict sailing delays between Vancouver and Victoria. The contest is open to all graduate and undergraduate students across Canada. In this project, I cleaned and transformed data, performed feature engineering, and implemented Logistic Regression and XGBoost to create predictive model. I placed 24th/65 teams on the private leaderboard
In this project, I collaborated with two classmates to analyze a dataset from Kaggle to predict house sales. We created a multiple linear regression model by comparing different feature selection methods and evaluated diagnostic plots and summaries. Our findings were summarised and presented in an executive report and PowerPoint presentation to a class of 70 students.
In this project, I scraped and analyzed data from Wikipedia's webpage of MCU films. I created a variety of visualizations to find trends in the data.
In this project, I collected data from Donald Trump's twitter through an API. I performed text preprocessing and exploratory analysis to draw insights on his tweeting behaviour
This is a project from STAT 240 (Introduction to Data Science) where I created webscraper that cleans and collects relevant course information from SFU's webpages (ex. Professor's name, exam locations, course ID, textbooks)
This is my first shiny app. I customized the layout along with buttons that plots different curves/slopes with the data sets provided by Dr. Campbell
Connected to an SQLite database in R and wrote queries to extract relevant information to perform calculations and create graphs to answer questions