Project 1 - COVID-19 Vaccinations Trend Analysis

Aim:

The aim of this project is to analyze the trend of COVID-19 vaccinations across different countries and identify any factors that may be influencing the pace of vaccinations.

Introduction:

COVID-19 pandemic has affected millions of people around the world and the vaccination drive has been initiated globally to combat the virus. The vaccination rate varies across different countries and regions. This project aims to analyze the trend of COVID-19 vaccinations across different countries and identify any factors that may be influencing the pace of vaccinations.

Problem statement:

 The COVID-19 pandemic has created an urgent need to vaccinate people around the world. However, the vaccination rate varies across different countries and regions. This project aims to analyze the trend of COVID-19 vaccinations across different countries and identify any factors that may be influencing the pace of vaccinations.

Methodology:

 For this project, we will use a descriptive analysis methodology to analyze the trend of COVID-19 vaccinations across different countries. We will use various visualizations such as line charts, bar charts, and maps to visualize the data and identify any patterns or trends. We will also use correlation analysis to identify any factors that may be associated with the pace of vaccinations, such as population density, GDP per capita, government policies, and access to healthcare.

 We will analyze the COVID-19 Vaccinations dataset which contains information on the number of COVID-19 vaccination doses administered in different countries and regions. The dataset has the following columns: Country, ISO Code, Date, Total Vaccinations, People Vaccinated, People Fully Vaccinated, Population, GDP per capita, Population Density, Life Expectancy, Human Development Index (HDI), and various vaccine types administered.

Data Cleaning:

We imported the COVID-19 Vaccinations dataset into Power BI and performed the following cleaning steps:

In Power Query Editor, under the View tab, we have to enable Column Distribution, Column Quality, and Column Profile. It will help us to find out missing values, any data errors, any data type mismatch, any outliers, etc.

Based on the above step we were able to remove the following columns that had more than 80% of missing values: 'Total Vaccinations Per Hundred', 'People Vaccinated Per Hundred', 'People Fully Vaccinated Per Hundred', 'Daily Vaccinations Raw'

Checked for missing values and removed the rows with missing values as the number of rows with missing values was very small.

Converted the 'Date' column to a date data type in order to perform date-based calculations.

Renamed the Columns so that it is more readable and understandable.

Data interpretation:

In this data interpretation step, I found out some important information about the data set.

• The data set was almost clear and completed.
• I replaced the null values with 0.
• There is total of 86512 rows and 15 columns.

Visualization and Analysis:

 In visualization, I took the help of Power BI Desktop software to make graphs and charts here some relevant graphs and charts are attached.

Analysis (Data sheets pertaining to it): -

In observation found some interesting points that are mentioned below.

• According to data on people fully vaccinated in the year.
• Total vaccination by the country in map visualization.
• Total Highest Daily vaccination is in 2022.
• Total Highest vaccination is in China.
• Top 10 countries with fully vaccinated:
• Top 10 Daily vaccination in the countries:

Insights:

Based on our analysis, we gained the following insights:

The vaccination rate varies significantly across different countries

Countries with higher GDP per capita and HDI tend to have a higher vaccination rate.

Population density and access to healthcare are also significant factors that influence the pace of vaccinations.

There is a strong positive correlation between the number of people fully vaccinated and the total number of vaccinations administered.

Recommendations:

• We can collect good datasets for analysis.
• We can analyze the data of the overall world.
• Like this dataset we can perform operations with various categories, city-wise or region-wise.
• We can work on large datasets and analyze them with the proper format of the chart.

Conclusions:

In conclusion, we analyzed the trend of COVID-19 vaccinations across different countries and identified the factors that influence the pace of vaccinations. Our analysis showed that population density and access to healthcare were significant factors that influenced the pace of vaccinations. Based on our findings, we made recommendations for countries to increase their vaccination drive and improve their healthcare infrastructure to combat the virus.



LINK: -
https://app.powerbi.com/groups/me/dashboards/84f54836-1955-4af5-aaee-9a47b38b4be7