humayunattar/Algerian_forest_fire_prediction
Domain:
environment and energyclimateRecord type:
projectsoftware
Creator:
hum
Host:
# 🔥 Algerian Forest Fires Prediction
### 🚀 End-to-End ML Project | Streamlit Deployment | Real-World Use Case
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## 🧠 Problem Statement
Forest fires are unpredictable, destructive, and costly.
This project solves a **real-world problem**:
👉 Predicting fire risk using meteorological data before it happens.
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## ⚡ What Makes This Project Stand Out
* 🔥 Full **end-to-end ML pipeline**
* 📊 Real-world dataset (Algerian Forest Fires)
* 🧪 Proper **EDA + Feature Engineering**
* ⚙️ Model optimization (Ridge Regression)
* 🌐 Interactive **Streamlit Web App**
* 📦 Clean, production-ready structure
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## 🎯 Live Demo (Add This ASAP)
> 🚨 If this is missing, your project loses 70% impact.
👉 streamlit-app-link
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## 📊 Dataset Overview
The dataset includes environmental and weather conditions:
| Feature | Description |
| ------------------ | ----------------------------- |
| Temperature | Atmospheric temperature |
| RH | Relative Humidity |
| Ws | Wind Speed |
| Rain | Rainfall |
| FFMC, DMC, DC, ISI | Fire Weather Index components |
| Region | Bejaia / Sidi Bel-Abbes |
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## 🧩 ML Pipeline
```mermaid
graph TD;
A[Raw Data] --> B[Data Cleaning]
B --> C[EDA]
C --> D[Feature Engineering]
D --> E[Scaling]
E --> F[Model Training]
F --> G[Evaluation]
G --> H[Deployment]
```
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## ⚙️ Tech Stack
* **Python**
* **NumPy, Pandas**
* **Scikit-learn**
* **Matplotlib / Seaborn**
* **Streamlit**
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## 🧠 Model Performance
| Metric | Value |
| ---------- | ---------------- |
| R² Score | High (Good Fit) |
| RMSE | Low |
| Model Used | Ridge Regression |
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## 🚀 Run Locally
```bash
git clone github.com
cd Algerian_Forest_Fires_Prediction
pip install -r requirements.txt
streamlit run app.py
```
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## 📂 Project …