Created a Web-app using Django (a python framework) and MySQL and deployed it on Heroku.The function of the App is to detect outbreaks in the world by collecting, analysing, processing data and removing noise from reliable news sources like WHO. To implement this we used a disease prediction algorithm using a Decision-tree, real-time disease related news feed using Google news API and a disease information portal using information from the National Health Portal.Users can log-in and Hospital and Pharmacy users can also access a data-feeding feature that updates the disease database. Used the Prototype Software Engineering Model, created an SRS and conducted Unit and UI Testing.

Created a working Database using SQL commands through PgAdmin and PostgreSQL to model the database used by a cell service provider containing customers information, pre-paid and post-paid plans offered, service providing towers in the city and customer-care centres information. Used stored procedures to prioritize plans that user has subscribed to, to choose the plan that the deductions should be made from.

Dr. Ignaz Semmelweis, a Hungarian physician born in 1818 and active at the Vienna General Hospital. If Dr. Semmelweis looks troubled it's probably because he's thinking about childbed fever: A deadly disease affecting women that just have given birth. He is thinking about it because in the early 1840s at the Vienna General Hospital as many as 10% of the women giving birth die from it. He is thinking about it because he knows the cause of childbed fever: It's the contaminated hands of the doctors delivering the babies. And they won't listen to him and wash their hands! In this notebook, we're going to reanalyze the data that made Semmelweis discover the importance of handwashing. Let's start by looking at the data that made Semmelweis realize that something was wrong with the procedures at Vienna General Hospital.

On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone on board, resulting in the death of 1502 out of 2224 passengers and crew. While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others. In this project, built a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc).

Carried out two different implementations of the problem of finding a match of a query image of a person in our gallery of images of people taken from cameras of a network. The first one was using the Locality-constrained Linear Coding (LLC) technique in which the image is divided into parts to extract the features, descriptors are coded and matching is done through SVM. The second one was Spatial-Temporal Re-ID, which used Part-Based Convolutional Baseline (PCB) for feature extraction along with the space-time information for the elimination of irrelevant images.