Analyzing air pollution data is crucial for understanding variations in atmospheric pollutant levels over time and across different geographical regions. In this project, we conducted a detailed analysis of air pollution data spanning from 2018 to 2023 and covering various countries.

Initially, we obtained a dataset containing information on pollution levels in various countries. This dataset included yearly data from 2018 to 2023, allowing us to explore trends over a significant period.

Before delving into in-depth analysis, we performed an initial exploration of the data. We identified the variables present in the dataset, assessed the quality of the information, and checked if any preprocessing was necessary.

To understand trends over time, we chose to visualize pollution data by country. We used line charts to represent the variation in pollutant levels each year. This approach provided a visual understanding of changes in pollution patterns for each nation under analysis.

We identified countries that showed notable variations in pollution levels over the years. This included countries that experienced sharp peaks in certain years, suggesting specific events or conditions influencing air quality.

The air pollution data analysis revealed intriguing patterns and significant variations in pollutant levels across different countries during the analyzed period. Effective data visualization provided valuable insights into trends, highlighting the ongoing importance of global-scale air quality monitoring and management. While the project did not include predictive analysis using machine learning models, the initial data exploration shed light on critical aspects of the air pollution issue.