nikhilmalgar/Algerian_Forest_Fire_Prediction
Domain:
environment and energyRecord type:
softwaremodel
Creator:
nik
Host:
# π₯ 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.
---
## π Features
- π― Predicts Fire Weather Index (FWI) using Ridge Regression
- π§ Uses **StandardScaler** for feature normalization
- π» Built with **Flask** and **Scikit-learn**
- π Interactive and modern **animated frontend**
- π Clean, responsive, and professional UI design
---
## π§ 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 |
---
## π Project Structure
```
βββ model/
β βββ ridge.pkl
β βββ scaler.pkl
β
βββ templates/
β βββ index.html
β βββ home.html
β
βββ notebooks/
β βββ 2.0-EDA And FE Algerian Forest Fires.ipynb
β βββ 3.0-Model Training.ipynb
β
βββ application.py
βββ requirements.txt
βββ README.md
```
---
## βοΈ 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 π **127.0.0.1 β¦