Background : Mathematics and Data Science
E-mail : euced.corro@gmail.com
LinkedIn : linkedin.com/in/ec-corro/
Location : Makati City, Philippines
This study is an exploration of the importing behavior of the Philippines using 2019 customs data. The data is part of the “Philippine Customs Imports dataset” made available by the BoC through their website.
This study explores how k-nearest neighbor (k-NN) classification can be implemented in a data set. In this study, the Pokemon data set obtained from Kaggle was used.
Quick, Draw! is an online game developed by Google Creative Lab in which a neural network guesses the drawing as the player draws the given image. In this study, we aim to answer the question, “Using machine learning models, can Quick, Draw! sketches be guessed correctly given only the first few points of the first stroke?” The data set used in this study contains 36 million Quick, Draw! sketches under 82 categories. The data set has an estimated total size of 53GB.
This study aims to determine the air quality profile of cities in India as this country dominates the world’s top 30 polluted cities based on IQAir AirVisual’s 2019 World Air quality Report. The dataset was obtained on AWS Registry open data source. The one used comprised of physical air quality real time data from different cities in India in the period of October 1-31, 2020. The global OpenAQ dataset used in this study has an estimated total size of 24GB.
This study aims to develop a regression model that will predict the short term air quality of identified polluted cities in India as this country dominates the world’s top 30 polluted cities based on IQAir AirVisual’s 2019 World Air quality Report. The dataset was obtained on AWS Registry open data source. The one used comprised of physical air quality real time data from different cities in India in the period of October to December 2020. In the study, Amazon EMR service was used using the PySpark kernel. The global OpenAQ dataset used in this study has an estimated total size of 160GB.
In this study, the data on the stock price of Jollibee Food Corporation (stock code: JFC), which is a stock listed in PSE, from January 2012 to December 2019 were used to develop a model and a method that would improve the accuracy of predicting stock prices.
In this study, the data on the daily closing prices from January 2015 to December 2020 of the Philippine Stock Exchange Composite index (code: PSEi), which tracks the performance of the top 30 most representative companies listed on the PSE, were used to develop a stock price prediction model that follows a reinforcement-based learning. This study was done in order to explore in detail the fundamental concepts of reinforcement learning.