Used genetic algorithm to produce population of weights
Used inverse network loss as fitness function
Weights improved after several generations
Hybrid model outperformed other classification algorithms
Used Tensorflow applied CNN to extract features from images with street views and use them to fine tune VGG16 model
Applied distance metric learning to retrieve images with satellite view but having same semantic information.
Model achieved 93% validation accuracy
Full stack web application deployed on Heroku
Used React.js for frontend, Django for backend
Used Postgres for relational database hosted on AWS.
Features include add to cart, checkout and payment using PayPal API
Admin can add/edit users and add/edit items.
Check out the live application here!
Mined 12 million tweets from Twitter API related to QANON and 2020 US Elections
Performed Sentiment analysis using VADER
Performed Topic Modeling using BERT
Conducted Location based Qanon user profiling
Mined 0.5 million tweets from Twitter API related to 2019 Canadian Elections
Extracted 100k unique users from the data set and calculated their attributes
Performed dimensionality reduction to yield 3 attributes
Applied K-means clustering with k=2 to isolate bot accounts
Identified 6% bot accounts and 94% Human Accounts
Mined 16 million tweets from Twitter API related to Brexit
Performed Topic Modeling using LDA provided in GENSIM package
Positive correlation between GBP and Twitter sentiment
Extracted key discussion topics using unsupervised Machine Learning
Created a GUI in processing using Java
Incorporated force feedback using Haply development kit
Application allowed guided navigation with auditory feedback
Users feel greater resistance in densely populated regions on graph
Mined 1 million tweets from Twitter API related to Covid-19
Performed Sentiment analysis and Topic Modelling
Public Sentiment was most negative in USA and neutral
Government was the main focus of discussion in all three countries