Many measurements have been taken to tackle the COVID-19 pandemic. Among which, wearing a face mask is one way to prevent spreading the virus. This work aims to detect if a person is wearing a mask or not. With this objective, a machine learning model is developed, which leverages transfer learning to detect mask. For collecting human faces with the mask, a subset (1000 images) of the MAFA dataset is used.

The objective of this project is sentiment analysis (i.e., Positive, Neutral, Negative) from the popular health and fitness app reviews. For this task, app reviews are collected from the google play store. 10 popular health and fitness apps are chosen. All around 12000 most recent reviews are collected. Ratings are considered as the measure/label of positive, negative, and neutral sentiment of the reviews. The collected data are preprocessed and trained using a transformer model.

This project aims to classify ratings of the hotel reviews. There are 5 ratings (i.e., class) in the dataset along with the reviews. The dataset is quite balanced among the 5 classes. The objective is to develop two different classification models i.e., a baseline and a state of the art(SOTA) model to compare the performance of classifying ratings. To uncover interesting insights from the data, different exploratory analysis have been performed.

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.

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.

The objective of this project is to detect intent from texts. For this, a benchmark dataset is used, which includes 7 intents (Search Creative Work, get weather, Book Restaurant, Play Music, Add to Playlist, Rate Book, Search Screening Event) and 14 thousand samples. Transfer learning has been leveraged to train a machine learning model. The model takes the raw texts, which are tokenized and vectorized to feed into the pre-trained model.