PhenomDetect: Detection of Air Hazards in the U. S.- SVM, Random Forest, Gradient Boost, XGBoost, KNN, LSTM, GRU, Tableau

This project is a team effort of Team ‘Vesper’ participated in the NASA International Space Apps Challenge'20. Our team took on the challenge of the automatic air hazard detection because we were inspired by the idea of building a tool that could potentially save many lives just by automatically analyzing data from a variety of sources and putting this analysis into the hands of key decision-makers, as well as the general public.

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Question Classification - SVM, Logistic Regression, LSTM, BERT, Doc2Vec, TF-IDF

The objective is to build a question classification model. The questions have six different categories such as: Description(DESC), Entity(ENTY), Abbreviation(ABBR), Human(HUM), Location(LOC), Numeric Value(NUM). To investigate different approaches, the following data is used (downloaded from https://cogcomp.seas.upenn.edu/Data/QA/QC/): Training set 5(5500 labeled questions) Test set: TREC 10 questions Different data analyses have been performed and four different models are trained. The models are the followings: Tf-Idf + SVM: Tf-Idf is used for vectorizing texts and a linear model (i.