COVID-19 Data Analysis

Overview

This project involves analyzing COVID-19 data to extract meaningful insights using Python libraries such as pandas and Plotly. The notebook includes steps for data preprocessing, exploratory data analysis, and generating visualizations to understand the spread and impact of the pandemic.

Features

• Importing and cleaning the dataset (country_wise_latest.csv)
• Exploratory Data Analysis (EDA) to uncover patterns and trends
• Visualizations created using Plotly, including:
  • Bar charts showing countries with the highest confirmed cases
  • Pie charts displaying the distribution of cases by WHO regions
  • Scatter plots for trends in new cases, deaths, and recoveries
  • Choropleth maps for 1-week percentage increases and mortality rates
  • Box plots for death rates by WHO region
  • Treemaps of confirmed cases by WHO region and country
• Insights into COVID-19 metrics such as recovery rates, mortality rates, and regional impacts

Dataset

The dataset country_wise_latest.csv contains COVID-19 statistics for various countries, including:

• Confirmed cases
• Deaths
• Recovered
• Active cases
• New cases, deaths, and recoveries
• Deaths per 100 cases and recoveries
• Weekly changes in confirmed cases
• WHO region classification

The dataset is sourced from Kaggle. Ensure the dataset is placed in the same directory as the notebook or update the file path in the code.

Prerequisites

• Python 3.x
• Jupyter Notebook or Google Colab
• Required Python libraries:
  • pandas
  • plotly

Installation

1. Clone this repository:

   git clone https://github.com/your-username/covid19-data-analysis.git

2. Navigate to the project directory:

   cd covid19-data-analysis

3. Install dependencies:

   pip install -r requirements.txt

Usage

1. Open the notebook in Google Colab or locally:
   • For Google Colab:
     • Upload the notebook file Covid_19_Data_Analysis.ipynb to your Google Drive.
     • Open the notebook in Colab and set the runtime to include GPU if necessary.
   • For local setup:

     jupyter notebook Covid_19_Data_Analysis.ipynb

2. Follow the steps in the notebook:
   • Load and clean the dataset
   • Perform exploratory data analysis
   • Generate visualizations for key insights

Results

• Bar chart showing the top 10 countries with the highest confirmed cases.
• Pie chart illustrating the distribution of confirmed cases by WHO region.
• Scatter plots for trends in new cases, deaths, and recoveries.
• Choropleth maps displaying weekly percentage increases and mortality rates.
• Box plot for death rates by WHO region.
• Treemap of confirmed cases by WHO region and country.