Classification of Heart Sound Recordings

The 2016 PhysioNet/CinC Challenge aims to encourage the development of algorithms to classify heart sound recordings collected from a variety of clinical or nonclinical (such as in-home visits) environments. The aim is to identify, from a single short recording (10-60s) from a single precordial location, whether the subject of the recording should be referred on for an expert diagnosis. Link to Kaggle

December 19, 2019

Detecting Parkinson’s Disease

Parkinson’s disease is a progressive disorder of the central nervous system affecting movement and inducing tremors and stiffness. It has 5 stages to it and affects more than 1 million individuals every year in India. This is chronic and has no cure yet. It is a neurodegenerative disorder affecting dopamine-producing neurons in the brain. In this project, I used XGBClassifier from the xgboost library scikit-learn API for XGBoost classification. Dataset used - UCI ML Parkinsons dataset

October 20, 2019

Catch me if you can

Web-user identification is a hot research topic on the brink of sequential pattern mining and behavioral psychology. Here we try to identify a user on the Internet tracking his/her sequence of attended Web pages. The algorithm to be built will take a webpage session (a sequence of webpages attended consequently by the same person) and predict whether it belongs to Alice or somebody else. The data comes from Blaise Pascal University proxy servers.