Earthquake Damage Prediction

Project Overview

This project predicts the extent of damage caused by earthquakes using machine learning algorithms. It leverages historical earthquake data to analyze various factors like magnitude, depth, and location, and their correlation with the resulting damage.

The project makes use of:

• Pandas for data manipulation and analysis
• Numpy for numerical operations
• Matplotlib and Seaborn for visualizing data and patterns
• Scikit-learn for implementing machine learning models (e.g., Linear Regression, Random Forest)

Project Structure

├── data/
│   ├── test_values.csv            # Test dataset containing features for prediction
│   ├── train_labels.csv           # Training dataset containing labels (target values)
│   └── train_values.csv           # Training dataset containing features for prediction
├── notebooks/
│   ├── Earthquake_Damage_Prediction.ipynb        # Jupyter notebook with code and visualizations
│   └── Earthquake_Damage_Prediction_copy.ipynb   # A copy of the notebook for additional testing or modifications
├── src/
│   └── prediction_model.py        # Python script for training and testing the prediction model
└── mini_project.pdf               # Project documentation report
└── requirements.txt               # List of required Python packages

Setup and Installation Clone this repository to your local machine:

git clone https://github.com/TejasWaghmare18/Earthquake-Damage-Prediction.git

Install the required dependencies:

pip install -r requirements.txt

If you're using Jupyter notebooks, install Jupyter:

pip install jupyter

Usage Running the Jupyter Notebook Navigate to the notebooks folder and start Jupyter:

jupyter notebook

Open Earthquake_Damage_Prediction.ipynb or Earthquake_Damage_Prediction_copy.ipynb and run through the cells to explore the data, visualizations, and model training process.

Running the Model Script To run the prediction model script directly, execute:

python src/prediction_model.py

This will train the model on the data and output predictions based on the features.

Dependencies The following Python packages are required to run the project:

pandas

numpy

matplotlib

seaborn

scikit-learn

You can install them by running:

pip install -r requirements.txt

Contributing Fork this repository.

Create a new branch (git checkout -b feature-branch).

Make your changes and commit them (git commit -am 'Add new feature').

Push to the branch (git push origin feature-branch).

Create a new Pull Request.

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

This `README.md` file includes all the necessary details for setting up, using, and contributing to the project. You can now use it in your GitHub repository.