In a recent smaller project, I analyzed data on patients infected with COVID-19 to see what factors seem to be common in those patients who were not able to survive the infection. These factors were mostly determined using data visualization and Random Forest. The two largest factors that determined if a person was to survive their affliction were age and how long they've been infected. Unexpectedly, the predictive performance of the various Random Forest model variants produced high accuracy results of about 85%-95% depending on the factors/variables being fed into the models.
The most recent large project attempted to replace the vocals of an audio file with the vocals of a specific speaker. The results did not produce the expected outcome. However, the learning process and approach used in this project does serve as the starting point for new and better ideas on how to perform the task.
My first large project involved using random forest to predict the movement/direction of stock prices for the next day (whether the stock price will increase or decrease).
The machine learning models did worked at the start of the coronavirus pandemic (late Feb. to early Mar.). However, as the pandemic continues to effect the economy, the models were not able to perform as well.
One smaller project examined taxi data to predict variables that affect taxi riders' decisions.
Another smaller project examined user information to predict which users are likely to be high usage customers.
- Examining COVID-19 Survival and Death Rates, and Predicting Survival of Infected Patients
- Vocal Replacement of Audio Recordings
- Prediction of Price Direction in Stocks
- Examining Taxi Data and Predicting Rider Decisions
- Predicting Which Customers To Be Large Consumers
My name is Dao Vang with Dao being similarly pronounce like the Dow Jones stock market index.
I am currently a SpringBoard student in the Data Science track.
Check out my personal website for a quick short story of my life!
Or visit me on my LinkedIn if you want to know my background!
Contact me if you want to discuss ideas or just want to chat about random stuff:
dv930@nyu.edu