• Objective/ Aim of the Internship project Work : -
We need a strong model that predicts how the virus could spread across different states and regions in India. The goal of this task is to build a model that predicts the spread of the virus in the next 7 days.
• Introduction : -
Corona Virus disease (COVID-19) is an infectious disease caused by a newly discovered virus, which emerged in Wuhan, China in December of 2019.
Most people infected with the COVID-19 virus will experience mild to moderate respiratory illness and recover without requiring special treatment. Older people and those with underlying medical problems like cardiovascular disease, diabetes, chronic respiratory disease, and cancer are more likely to develop serious illness. The COVID-19 virus spreads primarily through droplets of saliva or discharge from the nose when an infected person coughs or sneezes, so you might have heard caution to practice respiratory etiquette (for example, by coughing into a flexed elbow).
• Technical details about the proposed project :
In here we import a few important libraries that we shall use throughout the model. Pandas is an extremely fast and flexible data analysis and manipulation tool and allows you to allow you to store and manipulate tabular data. We also import visualisation libraries such as matplotlib, seaborn
Tasks to be performed:
1. Analyzing the present condition in India.
2. State wise analyzing covid-19 cases in India.
3. Forecasting the State-wise COVID-19 cases using Linear
Regression Algorithm.
• Analysing the present condition in India So, how did it actually start in India? The first COVID-19 case was reported on 30th January 2020 when a student arrived in Kerala, India from Wuhan, China. Just in the next 2 days, Kerela reported 2 more cases. For almost a month, no new cases were reported in India, however, on 2nd March 2020, five new cases of coronavirus were reported in Kerala again and since then the cases have only been rising.
Algorithm used : LINEAR REGRESSION ALGORITHM
Linear Regression is a machine learning algorithm based on supervised learning. It performs a regression task. Regression models a target prediction value based on independent variables. It is mostly used for finding out the relationship between variables and forecasting. Different regression models differ based on – the kind of relationship between dependent and independent variables, they are considering and the number of independent variables being used.
Linear regression performs the task to predict a dependent variable value (y) based on a given independent variable (x). So, this regression technique finds out a linear relationship between x (input) and y(output). Hence, the name is Linear Regression. In the figure above, X (input) is the work experience and Y (output) is the salary of a person. The regression line is the best fit line for our model.
• Requirements specification
o Windows OS/UNIX o Python Concepts o Jupyter Notebook o Libraries(Pandas,Numpy,Matplotlib) o Datasets o Algorithm