SanketMeghale/Algerian_Forest-Fire_Prediction_Machine_Learning_Project

# 🔥 Algerian Forest Fire Prediction — Machine Learning Project --- --- 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**. --- ## 📁 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)** --- ## 🎯 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** --- ## ⚙️ 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| --- ## 🧠 Machine Learning Concepts Used - Data Cleaning - One-Hot Encoding - Standardization (Scaling) - Linear & Regulari …