├── README.md
└── merged.pdf


/README.md:
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 1 | 
 2 | # 🌍 Air Pollution Data Analysis
 3 | 
 4 | 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.
 5 | 
 6 | ## 📁 Dataset
 7 | 
 8 | The dataset used in this project is a `.csv` file (example: `3b01bcb8-0b14-4abf-b6f2-c1bfd384ba69.csv`) that includes fields such as:
 9 | - `station`
10 | - `city`
11 | - `country`
12 | - `pollutant_avg`
13 | - `latitude`
14 | - `longitude`
15 | 
16 | ## 🔧 Technologies Used
17 | 
18 | - Python
19 | - Pandas
20 | - NumPy
21 | - Matplotlib
22 | - Seaborn
23 | 
24 | ## 📊 Visualizations Included
25 | 
26 | This project generates multiple types of visualizations to understand the dataset:
27 | 
28 | 1. **Bar Plot** – Average pollutant by station
29 | 2. **Pie Chart** – Distribution of entries by city
30 | 3. **Histogram** – Distribution of `pollutant_avg` values
31 | 4. **Scatter Plot** – Pollutant Average vs Latitude with country-wise hue
32 | 5. **Line Plot** – Pollutant Average across Longitudes
33 | 6. **Correlation Heatmap** – Relationship among numeric columns
34 | 7. **Box Plot** – Pollutant Average distribution per country
35 | 8. **Pair Plot** – Country-wise scatter relationships
36 | 9. **Outlier-Free Box Plot** – Refined pollutant averages by country
37 | 
38 | ## 🧼 Data Cleaning
39 | 
40 | - Missing numeric values are filled with column means.
41 | - Categorical missing values are filled with the mode.
42 | - Outliers are handled using the IQR (Interquartile Range) method.
43 | 
44 | ## 📈 Summary Statistics
45 | 
46 | The notebook also prints descriptive statistics using `df.describe()` to get insights into the dataset's distribution, central tendency, and spread.
47 | 
48 | ## 📂 Usage
49 | 
50 | To run this project:
51 | 
52 | 1. Clone the repository or download the `.ipynb`/`.py` file.
53 | 2. Make sure you have the required libraries installed:
54 | 
55 | ```bash
56 | pip install pandas numpy matplotlib seaborn
57 | ```
58 | 
59 | 3. Replace the CSV path in the `read_csv()` method with your dataset path.
60 | 4. Run the script or notebook.
61 | 
62 | ## 🧪 Sample Output
63 | 
64 | - Visual plots for easy understanding of trends
65 | - Insights into pollution averages by location
66 | - Detection and removal of outliers
67 | - Heatmaps showing correlation between numerical columns
68 | 
69 | 


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/merged.pdf:
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https://raw.githubusercontent.com/Aagaj/pythonproject/e8e917c65f8a3e6c757049a9dfb1943d31046ce9/merged.pdf


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