A lightweight Python package, and API, for accessing all of the world's most up-to-date and accurate ISO 3166-2 subdivision data, including: name, local/other name, parent code, type, latitude/longitude, flag and history.

Golang package that contains the data of world's countries, states and their cities name. It's easy for the end-user to get all the data by just calling the relevant functions

CovidTracker traces all the covid-19 cases all over the world.

Node package that contains the data of world's countries, states and their cities name. It's easy for the end-user to get all the data by just calling the relevant functions

Python package that contains the data of world's countries, states and their cities name. It's easy for the end-user to get all the data by just calling the relevant functions

A NestJS-powered REST API that continuously fetches and serves real-time global statistics from Worldometer. Provides structured endpoints for population, health, environment, energy, food, water, government, and social media data with automatic updates every 5 seconds.

This repository contains a Flutter app that fetches real-time global COVID-19 data using an API. The app provides comprehensive insights into total cases, recoveries, and deaths worldwide. It features a clean and intuitive user interface for easy data visualization and exploration. Feel free to modify it !

World Co2 Emissions Exploratory Data Analysis , Visualization & Story Telling Using Python

Correlations between various world data

This website, crafted with HTML, delivers an interactive dive into European health, emissions, mortality, and trade data, through engaging Tableau visualizations for insightful trend analysis and public policy discourse.

A web-based COVID-19 tracker that displays global and country-specific cases , recoveries , and deaths with real time data and visualizations.

A simple and stylish web app to explore details about any country. Search for a country and instantly see its flag, coat of arms, capital, population, languages, currency, and a Google Maps link .

Problem to solve: find the patterns for increased suicide rates (1985 to 2016) among different cohorts globally, across the socioeconomic spectrum, using exploratory data analysis. Using bivariate analysis, I try to determine if there is any relationship between two variables.