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https://github.com/priyadarshinijain/air-quality-data-analysis-and-visualization

# 🌍 Air Quality Data Analysis and Visualization
https://github.com/priyadarshinijain/air-quality-data-analysis-and-visualization

data-analysis jupyter-notebook python visualization

Last synced: 15 days ago
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# 🌍 Air Quality Data Analysis and Visualization

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README 

        
# Air-Quality-Data-Analysis-and-Visualization



## 📖 Project Overview



This project provides a comprehensive analysis and visualization of air quality data. The dataset includes crucial air pollution metrics such as *PM2.5, **PM10, **SO2, **NO2, **CO, and **O3, along with meteorological factors like **temperature* and *pressure*. The goal is to explore these metrics and visualize trends to gain insights into air quality variations over time.



## 📂 Repository Structure



- *[Air_quality_visualization.ipynb](Air_quality_visualization.ipynb)*: 

  - This notebook loads, explores, and visualizes air quality data using Python libraries such as pandas, numpy, and seaborn.

  

- *[Air_quality_EDA.ipynb](Air_quality_EDA.ipynb)*: 

  - Focuses on *Exploratory Data Analysis (EDA)* to uncover statistical relationships and trends in the data using visualizations.



- *[air_quality_csv.ipynb](air_quality_csv.ipynb)*: 

  - Performs initial inspections of the dataset, such as data type checks and summarizing the structure of the data.



## ⚙️ Installation



To get started with this project, follow these steps:



bash

git clone https://github.com/your_username/your_repository_name.git

pip install -r requirements.txt



## 🌍 Air Quality & Meteorological Data Analysis



This project focuses on exploring and visualizing various air quality and meteorological variables, providing insights into pollution levels over time and across different locations.



## 🚀 Getting Started



To get started, open any of the notebooks in *Jupyter Notebook, **Google Colab*, or any Python IDE that supports .ipynb files. You can run the cells to load, explore, and visualize the dataset.



### 📦 Prerequisites

Ensure you have the following dependencies installed:

- pandas

- matplotlib

- seaborn

- numpy



You can install them using:

bash

pip install pandas matplotlib seaborn numpy



## 📊 Data Overview



The dataset consists of multiple air quality and meteorological variables, including:



| Pollutant | Description |

| --------- | ----------- |

| *PM2.5* | Particulate matter smaller than 2.5 microns |

| *PM10*  | Particulate matter smaller than 10 microns |

| *SO2*   | Sulfur dioxide levels |

| *NO2*   | Nitrogen dioxide levels |

| *CO*    | Carbon monoxide levels |

| *O3*    | Ozone levels |

| *TEMP*  | Temperature |

| *PRES*  | Atmospheric pressure |



The data is sourced from various air quality monitoring systems and is preprocessed in the notebooks for analysis.



## 📈 Visualizations



The notebooks produce a variety of visualizations to provide insights into the air quality metrics:



- *Time Series Plots*: Visualize how pollutant levels fluctuate over time.

- *Heatmaps*: Show correlations between different pollutants and meteorological variables.

- *Bar Charts & Line Graphs*: Compare pollutant levels and trends across locations or periods.



### Sample Visualization:



Relationship of two features using scatter plot



![image](https://github.com/user-attachments/assets/bdac52ec-5f40-4503-89ff-f93137f6d6ce)



Here's what we can interpret from this plot:



Temperature Range: The temperature ranges from about -10 degrees to over 40 degrees Celsius.



Pressure Range: The pressure ranges from below 1000 millibars to around 1040 millibars.



Trend: The plot shows a trend where pressure generally decreases as temperature increases. This inverse relationship is typical in atmospheric studies where warmer air tends to be less dense and thus exerts less pressure.