The Algerian Forest Fires Dataset is a popular machine learning dataset used for classification (Fire vs Not Fire) and sometimes regression tasks involving fire risk prediction.
# 🔥 Algerian Forest Fire Prediction
A machine learning web application that predicts the **Fire Weather Index (FWI)** and classifies fire risk levels based on environmental and weather conditions.
Built using **Python, Scikit-learn, and Streamlit**, this project provides an interactive interface for real-time predictions.
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## 🚀 Live Demo
👉 Deploy easily using **Streamlit Community Cloud**
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## 📌 Features
* Predicts **Fire Weather Index (FWI)**
* Classifies risk levels:
* 🟢 Low
* 🟡 Moderate
* 🟠 High
* 🔴 Extreme
* Interactive UI with user inputs
* Real-time predictions
* Clean and simple dashboard
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## 🧠 Machine Learning
* Model: Ridge Regression
* Preprocessing: Standard Scaler
* Libraries:
* NumPy
* Pandas
* Scikit-learn
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## 📂 Project Structure
```
algerianforestfires/
│
├── app.py # Streamlit application
├── requirements.txt # Dependencies
├── models/
│ ├── ridge.pkl # Trained ML model
│ └── scaler.pkl # Scaler for preprocessing
└── README.md
```
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## ⚙️ Installation & Setup
### 1. Clone the repository
```
git clone
github.com
cd algerianforestfires
```
### 2. Install dependencies
```
pip install -r requirements.txt
```
### 3. Run the app locally
```
streamlit run app.py
```
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## 🌐 Deployment
This project is deployed using **Streamlit Community Cloud**:
1. Push your code to GitHub
2. Go to Streamlit Cloud
3. Select your repository
4. Set `app.py` as the main file
5. Deploy 🚀
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## 📊 Input Parameters
The model uses the following features:
* Temperature
* Relative Humidity (RH)
* Wind Speed (Ws)
* Rain
* FFMC
* DMC
* ISI
* Classes
* Region
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## 📈 Output
* **FWI Score** (numeric prediction)
* **Risk Level Classification**
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## 🛠️ Tech Stack
* Python
* Streamlit
* Scikit-learn
* Pandas
* NumPy
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## 🤝 Contributing
Contributions are welcome! Feel free to fork this repo and submit a pull request.
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## 📜 License
This projec …