COVID-19 Data Analysis using Python

CORONA VIRUS PANDEMIC DATA ANALYSIS AND PREDICTION MODELING USING DATA MINING TECHNIQUES:

📊 Project Overview

This project involves a comprehensive data mining and analytics approach focused on the COVID-19 pandemic. It showcases the efficiency of forecasting models in predicting pandemic trends and features an intuitive user interface for user interaction.

• Datasets Used: 2 real life data-sets in the format of CSV files, called : covid_19_data.csv and covid19_line_list_data_modified.

🎯 Key Achievements

• Forecasting Accuracy:
  • 🎯 90% accuracy for cases under 200
  • 📈 82% accuracy for cases between 200 and 1000
  • 📉 87% accuracy for cases exceeding 1000

📖 Introduction

COVID-19 is caused by the SARS-CoV-2 virus, which was first reported by the WHO on December 31, 2019, after a cluster of viral pneumonia cases in Wuhan, China.

🔍 Data Analysis Steps:

1. Importing Required Packages: Utilizing libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn.
2. Data Gathering: Collecting data from reliable sources (e.g., WHO, CDC).
3. Data Transformation: Cleaning and transforming data to meet analysis needs (Data Wrangling).
4. Exploratory Data Analysis (EDA): Analyzing trends and visualizing data to derive insights.

💻 Technology Stack:

• Programming Language: Python
• Data Visualization: Matplotlib
• User Interface: Tkinter Library

📸 User Interface Demonstration

Below are screenshots of the user interface showcasing the project's functionalities:

🚑 Health Guidelines

If you experience COVID-19 symptoms:

• 🏠 Stay Home: Rest and avoid contact with others.
• 😷 Wear a Mask: Protect others if you must be around them.
• 🩺 Seek Medical Attention: If you have severe symptoms (e.g., shortness of breath).
• 🧪 Get Tested: Regardless of vaccination status, especially if at high risk.