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rayfrancogrivera

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Processed big data (>300GB) in Amazon Web Services (AWS) Cloud to create a subdomain recommender system that is specific to each user. The recommender system was developed using frequent itemsets and collaborative filtering.

powerph-predicting-electricity-demand-of-the-philippines-using-neural-networks

Developed a deep learning model using Feedforward Neural Network (FNN), Long-Short Term Memory (LSTM), and Gated Reccurent Unit (GRU) to predict the Philippines’ electricity demand.

Jupyter Notebook

prevention-is-better-than-cure-webapp

Deployed web app of Prevention is Better than Cure using Dash and Nginx.

Jupyter Notebook

hero-replacement-webapp

Deployed web app of Hero Replacement using Dash and Nginx.

Python

hero-replacement-determining-the-best-alternatives-for-a-banned-hero-in-dota2

Developed a recommender system using unsupervised machine learning and information retrieval to short-list alternatives to a banned hero in DOTA 2. Presented the passion project at the 20th National Student-Faculty Conference on Statistical Sciences.

HTML

prevention-is-better-than-cure-estimating-illegal-drug-vulnerability-among-filipino-youth

Wrangled the 2013 Young Adult Fertility and Sexuality Study (YAFS) Survey to develop a machine learning model that predicts a Filipino youth’s propensity to various illegal drugs.

Jupyter Notebook

predicting-antarcticas-temperature-from-noaa-ghcnd-using-lstm

Wrangled big data on AWS using a dask cluster then implemented a deep learning model to predict Antarctica's temperature using the NOAA GHCN-D dataset.

Jupyter Notebook

rayfrancogrivera/user-specific-stackexchange-subdomain-recommender-system

rayfrancogrivera

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