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Tuesday Talk by Ms. Ankita Chopra, Ms. Ankita Sharma and Mr. Devesh Lowe


Jagan Institute of Management Studies organized Tuesday Talk on 18th June, 2019 at its Rohini campus

Ms. Ankita Chopra and Ms. Ankita Sharma presented a research paper titled “Model to predict rate of heart disease using Liner Regression. They discussed that Heart diseases are among some of fatal diseases and a leading cause of deaths worldwide. It is difficult to diagnose heart diseases well before time as it requires complicated study of patient’s health and medical experience and knowledge. Today, the health sector contains huge amounts of unprocessed patient’s data that can be significant in making decisions. Predictive Modelling is a type of data analytics technique that uses algorithms and regression models to analyse past trends and generate future outcomes. In this paper, Linear and Multi linear regression models are applied for predicting the risks of heart diseases.


JIMS Rohini Tuesday Talk by Ms. Ankita Chopra, Ms. Ankita Sharma and Mr. Devesh Lowe
JIMS Rohini

Mr. Devesh Lowe presented a paper titled “Unfurling the latest patterns of Entertainment Consumptions by Indian Audience: A twitter Sentiment Analysis” . He explained that Online Video programming has emerged on the Indian entertainment landscape and is pleasantly thriving on binge watching, the “anytime, anywhere” content. By far, Television has been the most watched and trusted media for Indian audience but the OTT media has given an alternative channel for entertainment. In this paper the authors investigated viewership metrics by adjudging opinion through Tweets and studying them qualitatively and quantitatively. Tweets have been captured for popular TV shows and trending web series in the year 2018. Results showcase growing fondness for web content and are indicative of web based video programming sustenance. The study is a first of its kind in India meant to explore the urban audience viewership pattern using Twitter Sentiment Analysis.

PGDM JIMS Rohini, Delhi
JIMS
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