mkharroub/ECE143-The-Impact-of-Natural-Disasters-on-U.S.-Real-Estate-Prices

Project Title: The Impact of Natural Disasters on U.S. Real Estate Prices

Project Description

This project analyzes how natural disasters such as hurricanes and wildfires affect real estate prices in impacted regions across the U.S. The goal is to provide insights into the economic impact of these events, which can inform policy-making, urban planning, and investment strategies.

Data Sources

• NOAA (National Oceanic and Atmospheric Administration)

  • Data on natural disasters in the U.S.
  • Website: NOAA's National Centers for Environmental Information
  • Specific Datasets: 1.NOAA's Storm Events Database:(https://www.ncdc.noaa.gov/stormevents/ftp.jsp)

• Federal Housing Finance Agency

  • Data on U.S. housing prices and related statistics.
  • Website: Federal Housing Finance Agency
  • Specific Datasets: 1. Annual House Price Indexes (Counties (Developmental Index; Not Seasonally Adjusted):(https://www.fhfa.gov/DataTools/Downloads/Pages/House-Price-Index-Datasets.aspx#qpo)

Usage

• Python Prerequisite

  • Ensure that you have Python version 3.9 or above

• Clone the GitHub Repository

  • Use command "git clone https://github.com/mkharroub/ECE143-The-Impact-of-Natural-Disasters-on-U.S.-Real-Estate-Prices/tree/main" in your local machine
  • Navigate to the repository folder using the command "cd ECE143-The-Impact-of-Natural-Disasters-on-U.S.-Real-Estate-Prices"
  • Install the dependencies using the command "pip install -r requirements.txt"

• Launch Jupyter Notebook

  • Ensure that Jupyter Notebook is installed.
  • Start Jupyter Notebook
  • This will open a new tab in your web browser with the Jupyter Notebook interface.

• Open and Run the Python Notebook

  • In the Jupyter Notebook interface, navigate to the directory where the Python notebook is located.
  • Click on the notebook file (usually with a .ipynb extension) to open it.
  • Run the notebook cells one by one or use the "Run All" option to execute all cells.

Third Party Packages

• Pandas - A powerful data manipulation and analysis library.
• GeoPandas: Pandas for geospatial data.
• Plotly: Interactive Python plots.
• Matplotlib: Core Python plotting.
• NumPy: Numerical computing powerhouse.

File Structure

• dataset: This folder contains all the datasets used in our project.
• plots: This directory contains HTML files or any other format generated from the code in our main notebook.
• png: This directory contains image files generated from the code in our main notebook.
• scripts: This folder contains Python files with utility functions used in our main notebook.
• main.ipynb: This is our main Jupyter Notebook file where our project code resides.
• README.md: This file can contain documentation, instructions, or any information relevant to our project. It's typically the first thing people see when they visit our GitHub repository.
• requirements.txt: This file lists all the Python packages and their versions required for our project. It allows others to easily set up the same environment.

Authors

• Avyakta Kalipattapu - avyaktaKR
• Keke Hu - cocoa-hu
• Mohammed Kharroub - mkharroub
• Rohan Shingre - Ulorewien

License

This project is licensed under the MIT License - see the LICENSE.md file for details