Project Overview

This project involves deploying machine learning models to predict fire weather indices in Algeria. It includes two versions: a Flask application and a Streamlit application, each utilizing a Ridge regression model trained on the Algerian forest fire dataset. Users can input weather and land conditions to receive fire weather index predictions.

Live Application Links

•	Flask App: https://algerian-fire-endtoendprediction-1.onrender.com
•	Streamlit App: https://algerianfire.streamlit.app/

•	Flask App: Uses app.py for its operation.
•	Streamlit App: Operates through main.py.

Directory Structure

•	Models/
•	ridge.pkl - Serialized Ridge regression model.
•	scaler.pkl - Serialized standard scaler for feature normalization.
•	Notebooks/
•	26.1-AlgerianFireClean.ipynb - Jupyter notebook for data cleaning.
•	26.2-ModelTraining.ipynb - Jupyter notebook for model training.
•	templates/
•	home.html - HTML template for displaying predictions.
•	index.html - Initial landing page template.
•	application.py - Flask application script that defines routes and server logic.
•	README.md - Documentation providing project setup and usage details.
•	requirements.txt - List of dependencies required for the project.

Flask Web Application

The Flask application provides a simple interface for entering weather and vegetation parameters, processed by a pre-trained Ridge regression model to predict the fire weather index.

Installation and Execution

1.	Install required Python packages:

pip install -r requirements.txt

2.	Start the Flask application:

python application.py

The server will run on localhost, accessible via http://localhost:5000/.

Using the Web Application

•	Navigate to http://localhost:5000/ to access the input form.
•	Input the required parameters:
•	Temperature (°C)
•	RH: Relative Humidity (%)
•	Ws: Wind Speed (km/h)
•	Rain: Rainfall (mm)
•	FFMC: Fine Fuel Moisture Code
•	DMC: Duff Moisture Code
•	ISI: Initial Spread Index
•	Classes: Fire severity class (0 or 1)
•	Region: Region code (0 or 1)
•	Submit the form to receive the fire weather index prediction at the endpoint /predictdata.

Outputs

The application returns the predicted fire weather index based on the input conditions, displayed on the webpage.

Additional Notes

•	Verify the correct placement of data files and model pickle files in their respective directories.
•	Modify Flask host and port settings in application.py if necessary for deployment requirements.

Dependencies

•	Flask
•	Pandas
•	Numpy
•	Scikit-Learn
•	Pickle

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