https://github.com/shaadclt/kozhikode-pollution-analysis
This project involves the analysis of pollution data in Kozhikode city, where the data is acquired using an API and visualized using Matplotlib in Jupyter Notebook. The project aims to gain insights into the pollution levels during a week and visualize the trends and patterns.
https://github.com/shaadclt/kozhikode-pollution-analysis
Last synced: 3 months ago
JSON representation
This project involves the analysis of pollution data in Kozhikode city, where the data is acquired using an API and visualized using Matplotlib in Jupyter Notebook. The project aims to gain insights into the pollution levels during a week and visualize the trends and patterns.
- Host: GitHub
- URL: https://github.com/shaadclt/kozhikode-pollution-analysis
- Owner: shaadclt
- Created: 2022-10-19T05:31:04.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-07-08T08:19:01.000Z (over 2 years ago)
- Last Synced: 2025-03-28T09:11:22.254Z (7 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 11.7 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Pollution Analysis of Kozhikode City
This project involves the analysis of pollution data in Kozhikode city, where the data is acquired using an API and visualized using Matplotlib in Jupyter Notebook. The project aims to gain insights into the pollution levels during a week and visualize the trends and patterns.
## Data Acquisition
The pollution data for Kozhikode city is acquired using Blue Sky Analytics API. The API provides real-time or historical pollution data for various locations in the city.
To run the analysis, you will need to obtain an API key from Blue Sky Analytics and include it in your code for data retrieval.
## Prerequisites
Before running the code, make sure you have the following dependencies installed:
- Python (3.x)
- Jupyter Notebook
- Pandas
- NumPy
- Matplotlib
- Requests (for making API requests)
## Getting Started
To get started, follow the steps below:
1. Clone the repository:
```bash
git clone https://github.com/shaadclt/Kozhikode-Pollution-Analysis.git
```
2. Change into the project directory:
```bash
cd Kozhikode-Pollution-Analysis
```
3. Install the required dependencies:
4. Run Jupyter Notebook:
```bash
jupyter notebook
```
5. Open the `Kozhikode Pollution Analysis.ipynb` notebook in Jupyter.
6. Run the notebook cells to acquire the pollution data using the API, perform data preprocessing, and visualize the data using Matplotlib.
## Analysis Overview
The notebook provides a step-by-step guide to analyze the pollution data in Kozhikode city. The analysis includes the following tasks:
- Acquiring the pollution data using the API
- Data cleaning and preprocessing
- Visualization of pollution levels using line plots, bar plots, or other Matplotlib visualizations
- Analyzing the trends and patterns in pollution levels across different areas of the city
- Drawing insights and conclusions based on the analysis results
## Results and Insights
Throughout the analysis, various Matplotlib plots are used to visualize the pollution data and showcase the findings. These visualizations may include the comparison of pollution levels in different areas, the temporal trends of pollution, or any other interesting observations. Feel free to refer to the notebook for detailed results and interpretations.
## Customization
You can customize the analysis to suit your specific requirements. For example, you can explore different visualization techniques provided by Matplotlib, analyze specific pollutant levels, or combine the pollution data with other datasets for deeper insights into the city's environmental conditions.
## License
This project is licensed under the MIT License. See the `LICENSE` file for more information.
## Acknowledgments
- This analysis is inspired by the need to understand pollution levels in Kozhikode city and raise awareness about environmental conditions.
## Contributing
Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.