This project aims to predict the probability of floods in various regions using deep learning techniques. It is part of the "Regression with a Flood Prediction Dataset" competition from Kaggle's Playground Series - Season 4, Episode 5. The model developed here uses artificial neural networks (ANNs) to learn from environmental and socioeconomic data and predict flood risks.
- Deep Learning Model: Utilizes TensorFlow and Keras to build and train a deep neural network.
- Data Preprocessing: Includes steps for handling missing values, outlier removal, feature scaling, and data transformation.
- Exploratory Data Analysis (EDA): Uses visualization tools to understand data distribution and relationships.
- Real-time Prediction: A Flask-based web application allows users to input data and get real-time flood probability predictions.
- Programming Languages: Python, HTML, CSS
- Libraries and Frameworks: TensorFlow, Keras, Flask, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn
- Tools: Jupyter Notebook, Visual Studio Code, Git
- Python 3.x
- Pip (Python package installer)
- Virtual environment tool (optional but recommended)
- Clone the repository:
git clone https://github.com/yourusername/flood-prediction.git cd flood-prediction
Create a virtual environment (optional but recommended):
bash
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python -m venv venv
source venv/bin/activate # On Windows use venv\Scripts\activate
Install required packages:
If you don't have a requirements.txt file, you can generate it using:
bash Copy code pip freeze > requirements.txt Then install the packages using:
bash Copy code pip install -r requirements.txt Run the Flask application:
bash Copy code python app.py Open your web browser and go to http://127.0.0.1:5000/ to use the application.
Usage Data Input: Enter the required features in the web form. Predict: Click the 'Predict' button to see the flood probability. Output: The app will display whether you are in danger of flooding and the probability score. Model Training The model is trained on the train.csv dataset. The training process includes handling outliers, feature scaling, and using a deep neural network architecture. The model is evaluated using the R2 score. Results The final model achieved satisfactory performance, with an R2 score indicating its ability to predict flood probabilities accurately. It demonstrated the effectiveness of using deep learning techniques for environmental risk assessment.
Future Improvements Enhance model accuracy by experimenting with more advanced architectures like LSTM or CNN. Integrate additional features or datasets that might affect flood prediction. Improve the web application's user interface and user experience. Contributing Contributions are welcome! Please fork this repository and submit a pull request.
License This project is licensed under the MIT License - see the LICENSE file for details.
Acknowledgments Special thanks to the Kaggle community for providing the competition and dataset. Appreciation to all contributors and maintainers of the open-source libraries used in this project. markdown Copy code
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Include Images or Diagrams: Add visual elements to your README, such as model architecture diagrams, EDA visualizations, or screenshots of the web application.
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Documentation: Provide clear documentation on how to set up the project, train the model, and use the application.
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Links: Include relevant links, such as those to the Kaggle competition, datasets, or any research papers referenced.
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Versioning: If applicable, mention the version of the project and update it as new features are added.
This README.md template should give potential users and contributors a clear understanding of your Flood Prediction project's objectives, setup, and usage.