SanketMeghale/Algerian_Forest-Fire_Prediction_Machine_Learning_Project
Domain:
environment and energyRecord type:
projectsoftware
Creator:
San
Host:
# 🔥 Algerian Forest Fire Prediction — Machine Learning Project
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A **Machine Learning project** that predicts the **Fire Weather Index (FWI)** for the **Algerian Forest Fires Dataset** using environmental features such as temperature, humidity, wind, and rainfall.
This project demonstrates a complete **end-to-end ML pipeline**, including preprocessing, feature engineering, regression model training, hyperparameter tuning, and deployment using **Python** and **Scikit-learn**.
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## 📁 Project Overview
The **Algerian Forest Fire Dataset** contains meteorological and fire danger indices collected from two regions:
- **Bejaia Region (North Algeria)**
- **Sidi Bel-Abbes Region (Northwest Algeria)**
The goal is to build a regression model that predicts the **Fire Weather Index (FWI)**, representing fire intensity and spread potential.
### Dataset Features:
- Temperature (°C)
- Relative Humidity (%)
- Wind Speed (km/h)
- Rain (mm)
- FFMC (Fine Fuel Moisture Code)
- DMC (Duff Moisture Code)
- DC (Drought Code)
- ISI (Initial Spread Index)
- Region Index (0 = Bejaia, 1 = Sidi Bel-Abbes)
- **FWI (Target Variable)**
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## 🎯 Project Objectives
1. Clean and preprocess dataset
2. Fix missing values & outliers
3. Encode categorical features
4. Scale features for model consistency
5. Train multiple regression models:
- **Linear Regression**
- **Ridge Regression**
- **Lasso Regression**
- **Elastic Net**
6. Compare model performance
7. Hyperparameter tuning using GridSearchCV
8. Save the best model using **Pickle**
9. Deploy using **Streamlit**
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## ⚙️ Tech Stack
| Component | Description |
|----------|-------------|
| **Language** | Python |
| **Libraries** | Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn |
| **Models** | Linear, Ridge, Lasso, Elastic Net |
| **Deployment** | Streamlit |
| **IDE** | Jupyter Notebook ,VS Code|
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## 🧠 Machine Learning Concepts Used
- Data Cleaning
- One-Hot Encoding
- Standardization (Scaling)
- Linear & Regulari …