Project Title: COVID-19 Data Processing and Analysis System

Description: This project involved the development of a database system designed to process and analyze COVID-19 data from CSV files provided by the World Health Organization (WHO). The system efficiently handles large datasets and presents critical statistics, including the number of deaths, infections, and vaccinations. It was developed to help users gain insights into the pandemic's global and regional trends through advanced data manipulation and analysis.

At its core, the system employs a highly efficient hash table data structure to optimize data storage and retrieval, ensuring fast and reliable access to key statistics. The database allows users to execute various commands to filter, search, and analyze specific subsets of the data, enabling detailed, custom views of the information. This functionality was integrated into a simple, yet powerful user interface (UI) that supports user interaction through command-line inputs.

The system was implemented using C++ and developed in VSCode, leveraging efficient memory management and algorithm design to handle large-scale COVID-19 datasets.

Technologies Used:

C++ VSCode (Development Environment) Hash Table (for fast data access and retrieval) CSV File Processing (for handling large WHO datasets) Features:

Custom Command Interface: Users can filter and search the data based on various parameters, such as country, date, and type of statistics. High Performance: Optimized hash table implementation ensures rapid processing and retrieval of large datasets. Comprehensive Data Analysis: Users can explore data trends for infections, deaths, and vaccinations across different time periods and regions. Impact: This project enabled users to analyze COVID-19 data in real-time, offering valuable insights into the pandemic’s progression. The system provided a user-friendly tool for filtering and examining detailed statistics, proving instrumental in public health research and decision-making processes.