Shounak-Chavan/Algerian-Forest-Fire-Prediction-ML
Domain:
environment and energyRecord type:
projectsoftware
Creator:
Sho
Host:
Practice ML project using Ridge, Lasso, and ElasticNet models on the Algerian Forest Fire dataset. Includes EDA, preprocessing, feature selection, scaling, and Flask deployment.
# 🔥 Algerian Forest Fire — FWI Prediction System
### *(Fire Weather Index Prediction using Ridge & Lasso Regression)*
This project is a **Machine Learning–based wildfire risk prediction system** that predicts the **Fire Weather Index (FWI)** — a critical metric for assessing wildfire danger. The system processes meteorological and forest data, applies advanced regression models, and delivers predictions through an interactive **Flask** web application.
The workflow includes comprehensive data cleaning, exploratory data analysis (EDA), intelligent feature engineering, correlation-based feature selection, data scaling, multiple regression model implementations, and a professional web UI for real-time predictions.
---
## 🚀 Features
- 📊 **Cleaned & Preprocessed** Algerian Forest Fires dataset
- 🔥 **FWI Prediction** using meteorological and forest parameters
- 📉 **Multiple Models** — Ridge, Lasso, ElasticNet, and Linear Regression
- ⚙️ **Production-Ready** — StandardScaler + Model persistence with Pickle
- 🌐 **Interactive Flask UI** with Bootstrap styling
- 📂 **Clean & Modular** project structure
- 📈 **Hyperparameter Tuning** with RidgeCV, LassoCV, ElasticNetCV
---
## 🛠 Tech Stack
- **Python**
- **Flask**
- **NumPy**
- **Pandas**
- **Scikit-Learn**
- **Seaborn**
- **Matplotlib**
- **Pickle**
---
## 📁 Project Structure
```
Algerian-Forest-Fire-Prediction-ML/
│
├── app.py
├── requirements.txt
├── README.md
├── .gitignore
│
├── Models/
│ ├── ridge.pkl
│ └── scalar.pkl
│
├── templates/
│ └── index.html
│
└── Notebooks/
├── Algerian_forest_fires_dataset_UPDATED.ipynb
├── Cleaned_Dataset
├── EDA.ipynb
└── ModelTraining.ipynb
```
---
## ▶️ How to Run Locally
### **1️⃣ Clone the Repository**
```
git clone github.com
cd Algerian-Forest-Fire-Prediction-ML
```
### **2️⃣ (Optional) Create Virtual Environment**
```
python -m venv venv
```
Activate the venv:
**Windows:**
```
venv\Scripts\activate
```
** …