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COVID-19 Data Analysis and Visualization

This repository contains a Jupyter Notebook that performs analysis and visualization of COVID-19 data along with World Happiness Report data. The notebook explores the relationship between various factors such as GDP per capita, social support, healthy life expectancy, freedom to make life choices, and the maximum infection rate of COVID-19 in different countries.

Dataset

The notebook uses two datasets:

  1. COVID-19 Dataset: The COVID-19 dataset provides information about the number of confirmed cases of COVID-19 in various countries. The dataset includes daily data and is obtained from the "covid19_Confirmed_dataset.csv" file.

  2. World Happiness Report Dataset: The World Happiness Report dataset provides information about various factors contributing to happiness in different countries. The dataset includes factors such as GDP per capita, social support, healthy life expectancy, freedom to make life choices, and more. The dataset is obtained from the "worldwide_happiness_report.csv" file.

Analysis and Visualization Tasks

  1. Task 1: Importing required libraries and confirming successful import.
  2. Task 2: Data Preprocessing
    • Task 2.1: Importing the COVID-19 dataset.
    • Task 2.2: Deleting unnecessary columns from the COVID-19 dataset.
    • Task 2.3: Aggregating the rows by country.
    • Task 2.4: Visualizing the data related to a specific country.
  3. Task 3: Calculating and Analyzing the Infection Rate
    • Calculating the infection rate for a specific country.
    • Task 3.1: Calculating and plotting the first derivative of the infection rate curve.
    • Task 3.2: Finding the maximum infection rate for India and China.
    • Task 3.3: Finding the maximum infection rate for all countries.
    • Task 3.4: Creating a new dataframe with the maximum infection rate.
  4. Task 4: Joining COVID-19 and World Happiness Report Datasets
    • Importing and preprocessing the World Happiness Report dataset.
    • Joining the COVID-19 and World Happiness Report datasets.
    • Calculating the correlation between different factors.
  5. Task 5: Visualization of Results
    • Task 5.1: Plotting GDP per capita vs Maximum Infection Rate.
    • Task 5.2: Plotting Social Support vs Maximum Infection Rate.
    • Task 5.3: Plotting Healthy Life Expectancy vs Maximum Infection Rate.
    • Task 5.4: Plotting Freedom to Make Life Choices vs Maximum Infection Rate.

The notebook utilizes libraries such as pandas, numpy, matplotlib, and seaborn for data manipulation, analysis, and visualization.