{"id":15131138,"url":"https://github.com/priyadarshinijain/air-quality-data-analysis-and-visualization","last_synced_at":"2026-02-06T04:31:54.265Z","repository":{"id":256629168,"uuid":"855972077","full_name":"priyadarshinijain/Air-Quality-Data-Analysis-and-Visualization","owner":"priyadarshinijain","description":"# 🌍 Air Quality Data Analysis and Visualization","archived":false,"fork":false,"pushed_at":"2024-09-11T19:22:45.000Z","size":8086,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-05T20:43:14.890Z","etag":null,"topics":["data-analysis","jupyter-notebook","python","visualization"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/priyadarshinijain.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-09-11T19:02:18.000Z","updated_at":"2024-09-11T19:23:40.000Z","dependencies_parsed_at":"2024-09-12T05:26:31.121Z","dependency_job_id":"2e20d893-58da-4925-a02b-9d782f4b1a7f","html_url":"https://github.com/priyadarshinijain/Air-Quality-Data-Analysis-and-Visualization","commit_stats":null,"previous_names":["priyadarshinijain/air-quality-data-analysis-and-visualization"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/priyadarshinijain%2FAir-Quality-Data-Analysis-and-Visualization","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/priyadarshinijain%2FAir-Quality-Data-Analysis-and-Visualization/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/priyadarshinijain%2FAir-Quality-Data-Analysis-and-Visualization/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/priyadarshinijain%2FAir-Quality-Data-Analysis-and-Visualization/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/priyadarshinijain","download_url":"https://codeload.github.com/priyadarshinijain/Air-Quality-Data-Analysis-and-Visualization/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247399898,"owners_count":20932876,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-analysis","jupyter-notebook","python","visualization"],"created_at":"2024-09-26T03:24:02.574Z","updated_at":"2026-02-06T04:31:54.231Z","avatar_url":"https://github.com/priyadarshinijain.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Air-Quality-Data-Analysis-and-Visualization\n\n## 📖 Project Overview\n\nThis 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.\n\n## 📂 Repository Structure\n\n- *[Air_quality_visualization.ipynb](Air_quality_visualization.ipynb)*: \n  - This notebook loads, explores, and visualizes air quality data using Python libraries such as pandas, numpy, and seaborn.\n  \n- *[Air_quality_EDA.ipynb](Air_quality_EDA.ipynb)*: \n  - Focuses on *Exploratory Data Analysis (EDA)* to uncover statistical relationships and trends in the data using visualizations.\n\n- *[air_quality_csv.ipynb](air_quality_csv.ipynb)*: \n  - Performs initial inspections of the dataset, such as data type checks and summarizing the structure of the data.\n\n## ⚙️ Installation\n\nTo get started with this project, follow these steps:\n\nbash\ngit clone https://github.com/your_username/your_repository_name.git\npip install -r requirements.txt\n\n\n\n## 🌍 Air Quality \u0026 Meteorological Data Analysis\n\nThis project focuses on exploring and visualizing various air quality and meteorological variables, providing insights into pollution levels over time and across different locations.\n\n## 🚀 Getting Started\n\nTo 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.\n\n### 📦 Prerequisites\nEnsure you have the following dependencies installed:\n- pandas\n- matplotlib\n- seaborn\n- numpy\n\nYou can install them using:\nbash\npip install pandas matplotlib seaborn numpy\n\n## 📊 Data Overview\n\nThe dataset consists of multiple air quality and meteorological variables, including:\n\n| Pollutant | Description |\n| --------- | ----------- |\n| *PM2.5* | Particulate matter smaller than 2.5 microns |\n| *PM10*  | Particulate matter smaller than 10 microns |\n| *SO2*   | Sulfur dioxide levels |\n| *NO2*   | Nitrogen dioxide levels |\n| *CO*    | Carbon monoxide levels |\n| *O3*    | Ozone levels |\n| *TEMP*  | Temperature |\n| *PRES*  | Atmospheric pressure |\n\nThe data is sourced from various air quality monitoring systems and is preprocessed in the notebooks for analysis.\n\n## 📈 Visualizations\n\nThe notebooks produce a variety of visualizations to provide insights into the air quality metrics:\n\n- *Time Series Plots*: Visualize how pollutant levels fluctuate over time.\n- *Heatmaps*: Show correlations between different pollutants and meteorological variables.\n- *Bar Charts \u0026 Line Graphs*: Compare pollutant levels and trends across locations or periods.\n\n### Sample Visualization:\n\nRelationship of two features using scatter plot\n\n![image](https://github.com/user-attachments/assets/bdac52ec-5f40-4503-89ff-f93137f6d6ce)\n\nHere's what we can interpret from this plot:\n\nTemperature Range: The temperature ranges from about -10 degrees to over 40 degrees Celsius.\n\nPressure Range: The pressure ranges from below 1000 millibars to around 1040 millibars.\n\nTrend: 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.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpriyadarshinijain%2Fair-quality-data-analysis-and-visualization","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpriyadarshinijain%2Fair-quality-data-analysis-and-visualization","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpriyadarshinijain%2Fair-quality-data-analysis-and-visualization/lists"}