Data Commerce

Data Commerce

Dhobale Vaishnavi Navanath, Gaikwad Pratik Digambar, Hon Rushikesh, Jagdale  Samruddhi Anil

Under The Guidance of Prof. Mundhe Bhalchandra B.

DEPARTMENT OF COMPUTER ENGINEERING

BACHELOR OF ENGINEERING (Computer Engineering)

Sahyadri Valley College of Engineering & Technology
At-Rajuri, Tal-Junnar, Dist-Pune (412 411)

Abstract

                   MERN based forecasting mechanisms have proved their significance to anticipate in perioperative outcomes to improve the decision making on the future course of actions. The MERN models have long been used in many application domains which needed the identification and prioritization of adverse factors for a threat.

                    Several prediction methods are being popularly used to handle forecasting problems. This study demonstrates the capability of MERN models to forecast the number of upcoming patients affected by COVID-19 which is presently considered as a potential threat to mankind. In particular, standard forecasting models,

1)                   such as linear regression (LR),

2)                   leastabsoluteshrinkage

3)                   selection operator (LASSO),

4)                  support vector machine (SVM),

5)                  exponential smoothing (ES)

Three types of predictions are made by each of the models, such as the number of newly infected cases, the number of deaths, and the number of recoveries in the next 10 days.