This repository hosts my Air Quality Analysis Dashboard, created using Power BI. It provides a comprehensive analysis of air quality metrics across different geographic locations using data sourced from data.gov.
The primary goal of this project is to assess air quality trends over time and across regions. It helps identify pollution hotspots and areas with cleaner air, supporting environmental initiatives and raising awareness. 🌱
- 📂 Data Source: CSV files from data.gov
- 📊 Visualization: Power BI
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📌 Metrics:
- 🌡️ Average Air Quality Index (AQI)
- 🌆 Highest and lowest pollutant levels by location
- 📅 Annual trends in air quality data
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💻 DAX Queries:
Specific DAX formulas used for calculations, such as:CALCULATE(SUM([DataValue]), YEAR([Date])=2024)
- Visual Representations:
- 🗺️ Geographic heatmaps showcasing pollution levels.
- 📈 Trend lines for air quality over years.
- 📊 Bar charts comparing key metrics.
- 🌟 Regions with the best and worst air quality levels.
- 📉 Year-over-year improvement or decline in air quality.
- 🍂 Seasonal patterns influencing pollution metrics.
- Gained expertise in integrating CSV data into Power BI. 💡
- Improved proficiency in DAX for dynamic data analysis. 🧠
- Enhanced understanding of air quality metrics and their societal implications. 🌍
The analysis reveals critical trends and disparities in air quality. These insights can inform environmental policy-making and public health strategies. 🌿
- 🌆 The impact of industrial zones on local air quality.
- 🏞️ Comparison of urban and rural air quality metrics.
This project marks a significant milestone in my data analytics journey. I look forward to receiving feedback and contributing to similar impactful projects. 🚀
📂 Attached: Explore the insights further by checking out the PDF file in this repository.