nikhilmalgar/Algerian_Forest_Fire_Prediction
Domaine:
environment and energyType de record:
softwaremodel
Créateur:
nik
HĂŽte:
# đ„ FWI Prediction Web App
A **Flask-based Machine Learning Web Application** that predicts the **Fire Weather Index (FWI)** using a **Ridge Regression model**.
This project showcases how to integrate an ML model with an interactive and beautifully designed web frontend for real-time predictions.
---
## đ Features
- đŻ Predicts Fire Weather Index (FWI) using Ridge Regression
- đ§ Uses **StandardScaler** for feature normalization
- đ» Built with **Flask** and **Scikit-learn**
- đ Interactive and modern **animated frontend**
- đ Clean, responsive, and professional UI design
---
## đ§ Tech Stack
| Component | Technology |
| ----------------------- | ---------------------------------------------------- |
| **Frontend** | HTML5, CSS3, JavaScript |
| **Backend** | Flask (Python) |
| **Machine Learning** | Scikit-learn (Ridge Regression, StandardScaler) |
| **Model Serialization** | Pickle |
| **Deployment** | Flask Server / Render / Hugging Face Spaces / Heroku |
---
## đ Project Structure
```
âââ model/
â âââ ridge.pkl
â âââ scaler.pkl
â
âââ templates/
â âââ index.html
â âââ home.html
â
âââ notebooks/
â âââ 2.0-EDA And FE Algerian Forest Fires.ipynb
â âââ 3.0-Model Training.ipynb
â
âââ application.py
âââ requirements.txt
âââ README.md
```
---
## âïž Installation & Setup
### 1. Clone the Repository
```bash
git clone github.com
cd fwi-prediction-app
```
### 2. Create and Activate Virtual Environment
```bash
python -m venv venv
venv\Scripts\activate # On Windows
source venv/bin/activate # On Mac/Linux
```
### 3. Install Dependencies
```bash
pip install -r requirements.txt
```
### 4. Run the Flask App
```bash
python application.py
```
Then open your browser and go to đ **127.0.0.1 âŠ