🌍 Air Pollution Data Analysis

This repository contains a data analysis and visualization notebook built using Python libraries like Pandas, NumPy, Seaborn, and Matplotlib. The dataset used contains information about pollution levels across various cities, stations, and countries.

📁 Dataset

The dataset used in this project is a .csv file (example: 3b01bcb8-0b14-4abf-b6f2-c1bfd384ba69.csv) that includes fields such as:

• station
• city
• country
• pollutant_avg
• latitude
• longitude

🔧 Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn

📊 Visualizations Included

This project generates multiple types of visualizations to understand the dataset:

1. Bar Plot – Average pollutant by station
2. Pie Chart – Distribution of entries by city
3. Histogram – Distribution of pollutant_avg values
4. Scatter Plot – Pollutant Average vs Latitude with country-wise hue
5. Line Plot – Pollutant Average across Longitudes
6. Correlation Heatmap – Relationship among numeric columns
7. Box Plot – Pollutant Average distribution per country
8. Pair Plot – Country-wise scatter relationships
9. Outlier-Free Box Plot – Refined pollutant averages by country

🧼 Data Cleaning

• Missing numeric values are filled with column means.
• Categorical missing values are filled with the mode.
• Outliers are handled using the IQR (Interquartile Range) method.

📈 Summary Statistics

The notebook also prints descriptive statistics using df.describe() to get insights into the dataset's distribution, central tendency, and spread.

📂 Usage

To run this project:

1. Clone the repository or download the .ipynb/.py file.
2. Make sure you have the required libraries installed:

pip install pandas numpy matplotlib seaborn

3. Replace the CSV path in the read_csv() method with your dataset path.
4. Run the script or notebook.

🧪 Sample Output

• Visual plots for easy understanding of trends
• Insights into pollution averages by location
• Detection and removal of outliers
• Heatmaps showing correlation between numerical columns