This repository hosts a Jupyter notebook titled "Fire_Detection_In_Nature_Park_final.ipynb", dedicated to the detection and analysis of fires in nature parks. The notebook leverages data exploration, visualization, and predictive modeling to understand and anticipate wildfire events.
- Fire detection in Nature park: An introductory section outlining the focus and objectives of the notebook.
- Problem Setting: A detailed discussion of the problem of wildfires in natural areas, covering both natural and human-induced factors. This section also includes relevant statistics and reports on the impact of wildfires.
- EDA (Exploratory Data Analysis): Comprehensive data exploration to identify patterns, trends, and insights from the wildfire data.
- Variables: Explanation of the data variables used in the analysis, including geographical coordinates and temporal factors.
- Visualization: Data visualization techniques used to represent the data and findings effectively.
To use this notebook:
- Clone the repository to your local environment.
- Ensure you have Jupyter Notebook installed or access to a platform that supports Jupyter notebooks (e.g., Google Colab).
- Open the
Fire_Detection_In_Nature_Park_final.ipynbfile. - Execute the notebook cells in order to follow the analysis and results.
We welcome contributions to enhance the notebook's analysis or extend its scope. For contribution guidelines, please see CONTRIBUTING.md.
This project is licensed under the MIT License - see the license file for more details.