Inspiration
What it doesIn this project, we collected historical disaster data and performed data preprocessing to clean and prepare the dataset for analysis. We utilized Python libraries such as pandas and scikit-learn for data manipulation and machine learning modeling.
For predicting disaster impact, we used regression and classification algorithms from scikit-learn to build predictive models. We split the data into training and testing sets, trained the models on the training data, and evaluated their performance using metrics like mean squared error.
To estimate and optimize resource requirements, we analyzed infrastructure, population distribution, and emergency response resources data. We used pandas for data analysis and developed algorithms to estimate the demand for supplies, personnel, and equipment based on population density and vulnerability indices.
For identifying shelter locations and planning evacuation routes, We mapped out evacuation routes, identified areas requiring emergency shelters, and assessed the vulnerability of communities to different types of disasters.
Throughout the project, we faced challenges related to data quality, integration, and model complexity. However, by leveraging Python programming and machine learning techniques, we were able to overcome these challenges and develop effective disaster response strategies to enhance community resilience and save lives.
Built With
- clustering
- googlecollab
- matplot
- numpy
- pandas
- python
- scikit-learn
- seaborn
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