CloudBurst-Prediction-Flask

Overview

This project is a web application designed to predict cloud bursts using a pre-trained Random Forest model. The application fetches current weather data from a weather API, uses this data to make predictions, and displays the results on a web page rendered with Flask.

Features

• Real-time Weather Data: Fetches current weather data from an external API.
• Machine Learning Prediction: Uses a pre-trained Random Forest model to predict the likelihood of a cloud burst.
• User-Friendly Interface: Displays predictions and weather data on a clean, responsive HTML page.

Project Structure

cloud_burst_prediction/ │ ├── app.py ├── model/ │ └── random_forest_model.pkl ├── static/ │ └── css/ │ └── styles.css ├── templates/ │ └── index.html ├── README.md ├── requirements.txt └── config.py

• app.py: Main application file that contains the Flask routes and logic for fetching weather data and making predictions.
• model/random_forest_model.pkl: Serialized pre-trained Random Forest model.
• static/css/styles.css: Stylesheet for the HTML pages.
• templates/index.html: Main HTML page to display the weather data and prediction results.
• README.md: This file, providing an overview and setup instructions.
• requirements.txt: List of required Python packages.
• config.py: Configuration file for API keys and other settings.

Setup Instructions

Prerequisites

• Python 3.7 or higher
• pip (Python package installer)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/cloud_burst_prediction.git
   cd cloud_burst_prediction

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

Running the Application

1. Run the Flask app:

   python app.py

2. Access the application:

   • Open your web browser and navigate to http://127.0.0.1:5000.

Usage

1. Upon accessing the web application, it will automatically fetch the current weather data using the configured weather API.
2. The fetched data will be used by the pre-trained Random Forest model to predict the likelihood of a cloud burst.
3. The prediction results and current weather conditions will be displayed on the main page.

Contributing

Feel free to submit issues or pull requests if you have suggestions or improvements.

License

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

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By following these instructions, you should be able to set up and run the cloud burst prediction web application locally. Enjoy predicting cloud bursts with real-time weather data!