My Projects

Summary

The Oildex Dashboard project aims to offer executives and investors a tool for gaining a comprehensive overview of the Oil market within specific selected year ranges. Beyond being a data visualization tool, this platform provides valuable insights into Oil price trends while facilitating comparisons with Schlumberger (SLB) and the S&P 500 stock prices. For a comprehensive understanding of the project’s motivation and a detailed description of the data, please refer to the proposal.

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Summary

In this project, I undertook a comprehensive analysis of the NYC Airbnb dataset, employing various Machine Learning models to predict the popularity of Airbnb listings. The primary proxy indicator used to gauge popularity was the “reviews per month” column from the dataset. This project was anchored on the assumption that this metric could effectively represent a listing’s popularity based on the available data. The significance of this endeavor lies in the potential for a Machine Learning model to uncover the influential factors behind an Airbnb listing’s popularity. Not only can this assist Airbnb hosts in crafting more compelling advertisements, but it also has the potential to enable Airbnb as a company to refine its business model by focusing on promising and selective listings. Ultimately, this approach enhances the user experience for renters, promoting a more positive Airbnb experience.

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Introduction

Air pollution is an escalating concern that significantly impacts the health and overall quality of life for Canadians. The levels of various air pollutants are continuously monitored across Canada through the National Air Pollution Surveillance (NAPS) Program. However, one notable challenge lies in the accessibility of this invaluable data to the general public. The NAPS data, though diligently collected on an hourly basis, is often stored in complex data files, making it less accessible and understandable for the average Canadian. To bridge this gap and empower citizens with insights into the changing trends of air pollution in Canada, I together with three other teammates from the Master’s in Data Science cohort 2023-2024, developed a user-friendly data visualization app.

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