Portfolio

• Regularization Techniques

  

  An exploration of regularization techniques from Ridge to LASSO to SCAD, on Forest Fires, Boston Housing, and randomly generated datasets.

  Python Machine Learning

• Hype Words: Social Media Mining and Classification

  

  Utilized web scraping techniques to curate live datasets of social media posts, then exploring classification techniques to understand social media sentiment around Blockchain in Healthcare. Successfully applied a transformer-based approach to text classification using BERT.

  Python Machine Learning Transformers SKLearn

• WMATA DC Metro Analysis

  

  Developed a Websocket off the WMATA API, run in an AWS instance alongside a Javascript-based webpage for live train and bus locations. Lambda functions utilized in tandem with main site to feed route information, timings, and predicted arrivals.

  Python AWS Javascript node

• Visualizing Bird Migrations

  

  Processing of geojson, csv, and netcdf files to showcase Swainson Hawk migration patterns.

  Kepler.gl

• Comparing Regressors

  

  Analysis of various regression approaches from localized kernel-based regression (LOWESS) to neural networks in their efficacy on predicting Boston Housing Prices.

  Python Machine Learning

• NOAA: Waves and Buoys Scraping

  

  Utilize Beautiful Soup to crawl NOAA API endpoints for Buoy and Wave data to then store and visualize on a web-page map via Javascript.

  Python

• Music Frequency Analysis

  

  A frequency exploration of songs from 1791, 1976, and 2021. Spectrographic analysis is done to see the differences between these three eras and genres.

  Python R

• El Nino/La Nina Time Series Analysis

  

  An exploration of how the El Nino Southern Oscillation (ENSO) can have public health implications globally, with analysis done using spectral density, periodograms, and autocorrelation function plots.

  Python R