This project is a data-driven exploration of global weather patterns across 252 world capitals. Using Python for basic data cleaning and analysis, and Power BI for creating visuals. The analysis highlights how temperature, humidity, and atmospheric conditions vary across continents in just a few weeks of real observations.

European Airbnb listings show clear geographic price differences, and in this project I explore the patterns that drive these variations. I cleaned and prepared the dataset and then used Tableau to visualise the key factors that shape the pricing across Europe.

COVID-19 reshaped global health statistics on an unprecedented scale, creating a rare opportunity to analyse how the pandemic evolved across countries in time. Through SQL analysis, I examined the core metrics of the pandemic to understand how different regions were affected. In the end of the project, the final insights were brought together in a Tableau dashboard that highlights the most important numbers and global patterns visually.

Predicting the future value of an investment is never straightforward, especially across multiple years of uncertainty. Using Python, I examined how Intel and SSAB-A historically behaved and simulated long-term price scenarios based on these patterns. The result is a forward-looking forecast that compares simulated outcomes with what actually happened in the market.