What is Covid-19?
A coronavirus is a type of virus that has crown-like spikes around it. That’s why it has the word “corona” in it. The word “corona” is Latin for “crown.”
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).
Let’s see a simple Visualization to demonstrate the state-wise coronavirus cases in India. This data fetched from Kaggle. Then data is represented in the bar graph and Pie graph and line graph.
Import the libraries that we need for Data Visualization
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 visualization libraries such as matplotlib, seaborn.
Reading the dataset
Reading covid-19 dataset of India by creating data frame using pandas
Cleaning the dataset
Now we will clean the dataset by checking for the NAN values
Filling the NAN values by using a method called “backward-fill” and “forward-fill”
Rechecking if there are any NAN values in the dataset
Analyzing Covid-19 cases in India
There are several types of graphs for visualizing the data.
Total cases State Wise using pie chart :
Visualization of the spread of covid-19 cases in India
We shall use Folium to create a map corresponding to the number of cases in different geographies.
State Vs Deceased cases Using Bar Graph:
State Vs Recovered cases Using Bar Graph:
State Vs Confirmed cases Using Bar Graph:
Heatmap using Seaborn :
Heatmap is a visualization that displays data in a color-encoded matrix.
Line Graph for Confirmed, Recovered, and Deceased cases :
These are all the India State-wise Visualization of Covid-19 cases.
Machine Learning Model:
Applying k-means clustering model to the data. Assuming the data is divided into 5 clusters.
Consider the necessary features to built the model.
Import KMeans from sklearn library .Fit the model to the data and predict the clusters.
Adding the cluster column to the data by adding plus one to the clusters as the clusters start from zero.
Plotting scatter plot between ‘Confirmed’ and ‘Recovered’ to observe the clusters.
Conclusion:
By observing the above plot, the state which has black cluster(Maharashtra) should be given vaccine first ,next blue cluster(Tamil Nadu) and next red clusters(Karnataka, Delhi and Andhra Pradesh) and next yellow clusters and last green clusters based on the availability of vaccine.
