https://github.com/pradeish29/cloud-burst-predict-streamlit
cloud burst prediction website rendered using streamlit from a pretrained ml model
https://github.com/pradeish29/cloud-burst-predict-streamlit
cloudburst cloudburst-prediction jupyter-notebook ml openweathermap-api prediction-model python rainfall-prediction randomforest streamlit
Last synced: about 1 month ago
JSON representation
cloud burst prediction website rendered using streamlit from a pretrained ml model
- Host: GitHub
- URL: https://github.com/pradeish29/cloud-burst-predict-streamlit
- Owner: pradeish29
- License: mit
- Created: 2024-02-10T08:43:07.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-04-16T23:41:54.000Z (about 2 years ago)
- Last Synced: 2025-03-26T13:18:02.246Z (about 1 year ago)
- Topics: cloudburst, cloudburst-prediction, jupyter-notebook, ml, openweathermap-api, prediction-model, python, rainfall-prediction, randomforest, streamlit
- Language: Jupyter Notebook
- Homepage: https://cloud-burst-prediction.streamlit.app/
- Size: 23.1 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Cloud Burst Prediction using Random Forest Model and Streamlit
## Overview
This project aims to predict cloud burst occurrences using a Random Forest model deployed via a Streamlit web application. The model utilizes real-time weather data from OpenWeather.org, allowing users to input a city name to retrieve relevant weather information and receive a prediction output.
## Deployment
this project has been deployed using streamlit cloud.
https://cloud-burst-prediction.streamlit.app/
## Page view
## Features
- **Real-time Weather Data**: Integration with OpenWeather.org provides up-to-date weather information.
- **User-Friendly Interface**: Streamlit app allows users to easily input city names and view prediction outputs.
- **Random Forest Model**: Utilizes a trained Random Forest model to predict cloud burst occurrences based on weather data.
## Usage
To use the application:
1. Clone the repository to your local machine.
2. Install the necessary dependencies using `pip install streamlit joblib requests bz2file`.
3. Check and modify the path for certain files in the code
4. Run the `model.ipynb` file
5. Run the Streamlit app by executing `streamlit run app.py` in your terminal.
6. Access the app via the provided local URL in your browser.
7. Enter a city name to retrieve weather data and receive the cloud burst prediction output.
## File Structure
- `app.py`: Streamlit application script.
- `model.ipynb`: Pre-trained Random Forest model for cloud burst prediction.
- `model.joblib`: For importing and exporting the Random Forest model
## Data Sources
- [OpenWeather.org](https://openweathermap.org/): Provides real-time weather data.
## Credits
- **Developed by**: Pradeish Misara
## License
This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details.
Feel free to customize this template further according to your project's needs!