Graduated from NITRR. Currently building at fold.money. A tool that tells stories about your money.
I am Data Science Enthusiast and have previously worked at various startups building on my skills and meeting a lot of like-minded folks!
When I'm not in front of a computer screen, I'm probably, infront of my phone binging. I think its time to change my lifestyle 😅
Worked in a small team to create the beautiful tagging engine with F1 score of 95%!
A website which based on symptoms entered by user gives the probable disease he/she may have and recommends nearby hospitals/clinics (within 10 KM).
This webapp lets user to listen to songs based on realtime emotion detected through mobile camera or web camera and helps our users to interactively chat with similar users matched on the emotion they currently feel.
Chrome extension to highlight relevant items based on your search query.
This project demostrates the use of Transfer Learning using VGG Network to classify whether the cell is infected or not.The app was made on Streamlit and has been deployed as a docker wrapper on cloud using two step deployment process.
This is a deployed app which suggests movies based on the similarity of their content. The similarity metric that was used is cosine similarity.
Built a book recommendation engine using collaborative filtering technique with k-means clustering.The similarity in taste of two users is calculated based on the similarity in the rating history of the users.
With the help of transfer learning trained InceptionV3 model to classify 26 different gestures mapping 26 Alphabets of English Language
Using several models made a vote classifier based on hard voting in which predictions are the majority vote of contributing models.
Built a simple Generative Adversarial Network to generate image of digit 5 from noise distribution.
Trained a LSTM neural network with Word2vec Embeddings of 300 dimensions to classify words into thier parts of speech.