Goal of the analysis is the early detection of sepsis using physiological data. The early prediction of sepsis is potentially life-saving, and we aim to predict sepsis 6 hours before the clinical prediction of sepsis. Late prediction of sepsis is potentially life-threatening, and also consumes heavy hospital resources.

By predicting sepsis in non-sepsis patients or predicting sepsis very early in sepsis patients consumes limited resources and we can assume the risk of prediction to be minimal but revolutionary.

Description goes hereThere are a number of techniques for the detection of human sentiments using channels such as voice and facial expressions. Many a times these channels can be faked with or without the knowledge of the person. Therefore, making use of a reliable system such as Electroencephalography to record physiological signals becomes vital. The main objective of this project is to find the sentiment of the subject after applying the models formulated.

The energy output in a power plant is highly dependent on the environmental changes that occur at the location. If the energy outputs are predicted accurately, then the energy suppliers can collaborate with other sources to minimize the cost and get efficient production of energy. In this project, we are fitting a Linear Regression formula, that can be used to predict the Energy Output at a Power Plant by comparing the relationship between various factors such as Ambient Temperature, Ambient Pressure, Vacuum, Relative Humidity. The analytics are performed on a dataset obtained from a Combined Cycle Power Plant (CCPP). The model build gives a very accurate prediction of Energy Output for any newly supplied dataset with the same or similar dataset. Finally, a graph will be plotted which shows the closeness of the predicted energy output with the actual energy output in the dataset.

A satellite map image contains huge amount of data/ information in it. With every passing day, new objects get added into or removed from these satellite map images. It is very important to identify this information and sort it for an accurate navigation system. Our objective is to reduce this labor work and automate this task.

With this project our main motive is to train a neural network to learn about a particular object present in the map image and detect its location in the image. We are starting off with water bodies.

Implement a web application that translates American Sign language and displays the prediction results as text to the user. The project also fulfills the task of real time data collection from the web camera, connected to the PC. The sign captured from it is sent through the trained model to predict the nature of the sign.

I will be using time series forecasting technique to forecast champagne sales. The sales data of champagnes of a company names 'Perrin Freres'. The solution are built in a Python notebook with a step by step walkthrough of my intuition while analyzing the data

The main objective is detection of single bit error, burst error and to correct by using the suitable Methodology (by hamming distance concept). The purpose of this project is to visualize unreliable Transmission of data during the transmission from encoder to decoder. It involves block coding, finding the hamming distance between to existing code words, redundancy, error correction, parity check code.

MiniTwit is an example application that ships with the Flask micro framework for Python. It implements a subset of Twitter’s functionality, allowing users to post new messages and follow messages posted by other users.

This projects aims to build a web application to facilitate people in finding partners to play a sport. Partner search is based on individual preferences. (Favorite Sport, Location, Available Time Schedule, Sport Expertise level, Age, Sex, and etc.)