The objective of this project is to classify news category from articles. The input data consist of 2225 news articles from the BBC news website corresponding to stories in 5 topical areas (e.g., business, entertainment, politics, sport, tech). LSTM has been applied in the classification task to categorize articles. TensorFlow 2.0 has been used to train the model. Word embedding is used in feature generation. TSNE is used to visualize the word vectors in 2d space.

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.

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.

Forecasting energy consumption can be crucial to have a sense of how much energy/power needs to be produced. The objective of this work is to predict future monthly energy consumption from time-series data. The data includes hourly energy consumption of different states in the US collected by a regional transmission organization - PJM. Different exploratory data analysis are performed on the data to detect the daily, weekly, monthly, and yearly trends.