Earthquake Prediction Dashboard - Spark, Tableau, MongoDB

The objective is to report the prediction of the earthquake from the historical data. A machine learning model is trained with historical data of the world related to earthquakes from 1965-2016. The data includes geographical location and magnitude of the earthquakes (23.5k samples). The model predicts earthquake magnitude for the year of 2017. Finally, a dashboard is created to visualize the prediction in addition to the historical analysis on the data.

April 2, 2017

Fake or Real Tweets - BERT, LSTM, TF-IDF

The dataset includes tweets about disasters, e.g., earthquake, wildfire. The objective is to detect if the tweet is about a real disaster vs. fake disaster. Different approaches have been performed for data cleaning and training the model. The best model can predict real vs. fake tweets with 89% accuracy using transfer learning (BERT). The following models have been developed for training: BOW Model with Logistic Regression. (accuracy 77%) Tf-Idf with Logistic Regression.

April 1, 2017

Weather Prediction - Bidirectional LSTM

This project predicts weather (i.e., min-max temperature) from historical data. The dataset includes hourly inputs of pressure, humidity, temperature, wind speed, and wind direction of 36 cities from the year 2012 to 2017. From the dataset preprocessing is done to engineer attributes to predict min and max temperature of Toronto. Data from 2012-2016 is used as the training dataset, while the attempt is to predict the min and max temp of 2017.