A predictive model project to estimate economic losses from natural disasters globally over the next 25 years using machine learning and statistical modeling.
This project, "Forecast 2050", aims to forecast the economic impact of natural disasters globally over the next 25 years. Utilizing a combination of machine learning techniques and statistical models, we aim to provide insights that can help policymakers and businesses prepare and mitigate the financial risks associated with these events.
- Time Series Forecasting: Using historical data to predict future economic impacts.
- Data Visualization: Graphical representation of data and forecasts to aid in interpretation and decision-making.
- Scenario Analysis: Tools that allow users to visualize potential economic outcomes under different scenarios.
This project uses multiple data sources including:
- Historical economic data from the World Bank.
- Natural disaster data sets from NOAA (National Oceanic and Atmospheric Administration).
- Climate change projections from the IPCC (Intergovernmental Panel on Climate Change).
To set up your local environment to run this project, follow these steps:
- Python 3.8 or higher
- pip and virtualenv
Clone the repository and install the required packages:
git clone https://github.com/yourusername/forecast-2050.git
cd forecast-2050
python -m virtualenv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
pip install -r requirements.txt