Covid Dataset of Different Regions
Objective
The Machine Learning objective of a Covid dataset of different regions is to compile and analyze data related to Covid-19 cases, deaths, recoveries, and vaccinations across various geographical areas. This information helps in understanding the spread and impact of the virus, guiding public health decisions, and formulating strategies to combat and manage the pandemic.
Abstract
The novel coronavirus outbreak was first reported in late December 2019 and more than 7 million people were infected with this disease and over 0.40 million worldwide lost their lives. The first case was diagnosed on 30 January 2020 in India and the figure crossed 0.24 million as of 6 June 2020. Machine learning (ML) based forecasting mechanisms have proved their significance to Anticipate in perioperative outcomes to improve the decision making on the future course of actions. In this project we are using Multiple Logistic regression algorithm as our proposed algorithm by comparing with linear regression, Lasso Regression, Logistic Regression and Bayesian Ridge Regression algorithms. In this project the dataset was taken from publicly available dataset. Our proposed algorithm outperforms the remaining algorithms in terms of r2 scores.
KEYWORDS: Machine Learning, Covid-19, Multiple Logistic Regression, Regression, r2score
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
H/W CONFIGURATION:
Processor: I3/Intel Processor
RAM : 8GB (min)
Hard Disk: 128 GB
Key Board: Standard Windows Keyboard
Mouse : Two or Three Button Mouse
Monitor : Any
S/W CONFIGURATION:
Operating System: Windows 7+
Server-side Script: Python 3.6+
IDE : Colab
Libraries Used: Pandas, Numpy, Scikitlearn, tensorflow,
Related Projects
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